LAPORAN IV PRAKTIKUM MACHINE LEARNING AND MODERN PREDICTION

BAB I PENDAHULUAN

1.1 Latar Belakang

Ensemble Learning merupakan sebuah teknik yang melibatkan penggunaaan beberapa model Machine Learning yang difungsikan untuk memprediksi, klasifikasi, atau menyelesaikan masalah. Tujuan utama dari ensemble learning ini adalah untuk meningkatkan kinerja prediksi atau generalisasi model melalui kombinasi beberapa model yang berbeda. Konsep dasar dari ensemble learning adalah gabungan atau kombinasi dari beberapa model yang cukup baik dalam melakukan prediksi atau klasifikasi, sehingga dapat menghasilkan output yang lebih baik dibandingkan dengan menggunakan satu model saja (Al-Husaini dkk., 2024).

Salah satu teknik Ensemble Learning adalah Boosting. Boosting adalah teknik pembelajaran mesin yang menggabungkan beberapa model lemah untuk membuat satu model yang kuat. Ide teknik ini adalah melatih serangkaian model lemah secara berulang-ulang dan kemudian menggabungkannya menjadi satu model yang kuat. Selama proses pelatihan, model-model tersebut diberi bobot berdasarkan keakuratannya, di mana model yang lebih akurat menerima bobot yang lebih tinggi. Teknik ini memastikan bahwa model akhir merupakan rata-rata tertimbang dari masing-masing model, dengan model yang lebih akurat memiliki dampak yang lebih besar pada hasil akhir. Melalui penggabungan beberapa model lemah dengan cara ini, Boosting dapat meningkatkan akurasi keseluruhan sistem pembelajaran mesin dan membuatnya lebih tangguh terhadap variasi dalam data masukan (Novaliendry, 2025).

Pada pertemuan kali ini, praktikan akan mempelajari Adaboost, yaitu algoritma yang didasarkan pada teknik Boosting. Ide sederhananya adalah beberapa model kecil dilatih secara berurutan, dan setiap model baru dibuat untuk memperbaiki kesalahan model sebelumnya. Di setiap putaran, data yang sering salah diprediksi akan diberi bobot lebih besar sehingga mendapat perhatian lebih pada model berikutnya. Melalui Adaboost model-model yang tadinya kurang akurat jika berdiri sendiri dapat digabungkan menjadi satu model yang jauh lebih baik (Roza dkk., 2023).

1.2 Rumusan Masalah

Berdasarkan latar belakang, rumusan masalah yang dapat disimpulkan adalah:

1. Bagaimana metode Boosting sebagai metode ensemble learning?
2. Bagaimana cara mengimplementasikan Boosting untuk kasus klasifikasi dan regresi menggunakan R?
3. Bagaimana cara mengevaluasi performa model menggunakan ukuran evaluasi yang sesuai?

1.3 Tujuan

Adapun tujuan penelitian ini yaitu:

1. Mahasiswa dapat memahami Boosting sebagai metode ensemble learning.
2. Mahasiswa dapat mengimplementasikan Boosting untuk kasus klasifikasi dan regresi menggunakan R.
3. Mahasiswa dapat mengevaluasi performa model menggunakan ukuran evaluasi yang sesuai.

BAB II TINJAUAN PUSTAKA

2.1 Ensemble Learning

Ensemble Learning adalah teknik dalam Machine Learning yang menggabungkan dua atau lebih algoritma untuk meningkatkan akurasi, stabilitas, dan mengurangi kesalahan atau bias dari model individu. Ensemble Learning ini merupakan teknik yang mengombinasikan algoritma Machine Learning untuk memperoleh performa yang lebih baik dibandingkan penggunaan algoritma tunggal. Secara umum, tujuan utama ensemble learning adalah menciptakan model prediktif yang lebih akurat dan andal dengan cara menggabungkan kelemahan dan kekuatan dari berbagai model. Teknik ini sering digunakan karena mampu meningkatkan akurasi prediksi serta mengurangi risiko overfitting. Oleh karena itu, ensemble learning menjadi pendekatan yang efektif dalam mencari solusi prediksi terbaik dibandingkan penggunaan satu algoritma saja (Dyasa, Satria, & Adiputra, 2026).

2.2 Boosting

Boosting merupakan variasi perkembangan dari bagging. Tujuan dari penggunaan metode Boosting adalah untuk meningkatkan performa dari model yang dibangun sebelumnya melalui proses dimana setiap model individu dibangun secara berurutan, kemudian dilakukan pengulangan dari sebelumnya. Pada data prosesnya, model berikutnya menekankan poin data yang salah pada klasifikasi model sebelumnya. Penambahan poin data yang salah pada pelatihan selanjutnya bertujuan untuk meningkatkan akurasi model secara keseluruhan. Pada model kedua, poin yang diklasifikasikan atau diprediksi salah ditambahkan ke sampel bootstrap kedua setelah model pertama selesai. Selanjutnya, model ensemble diterapkan pada dataset uji (Widiharso, 2024).

Berbeda dengan Bagging, model-model pada Boosting tidak dibangun secara independen. Pada Bagging, banyak model dapat dibentuk secara paralel dan kemudian hasil prediksinya diagregasi. Pada Boosting, learner ke-2 dibangun berdasarkan informasi dari hasil learner ke-1, learner ke-3 memperbaiki hasil yang masih belum baik dari tahap sebelumnya, dan proses tersebut dilanjutkan secara berurutan. Prinsip sequential learning inilah yang menjadi salah satu ciri utama Boosting.

Model-model sederhana yang dibangun pada setiap tahap tersebut disebut weak learner. Weak learner memiliki kemampuan prediksi yang terbatas apabila digunakan sendiri, tetapi setiap learner diarahkan untuk membantu memperbaiki kesalahan model yang telah terbentuk sebelumnya. Setelah sejumlah weak learner digabungkan, diperoleh model akhir yang lebih kuat, yang disebut strong learner.

Pada Boosting berbasis pohon, decision tree sering digunakan sebagai base learner. Kedalaman pohon dapat dibatasi sehingga pohon tetap sederhana dan berperan sebagai weak learner. Selanjutnya, kontribusi dari pohon-pohon tersebut digabungkan secara bertahap sehingga membentuk model Boosting akhir. Berikut adalah perbedaan Bagging dan Boosting (Fransiska, 2026):

Aspek	Bagging	Boosting
Cara membangun model	Paralel/relatif independen	Sequential; learner berikutnya bergantung pada hasil sebelumnya
Data tiap learner	Umumnya sampel bootstrap	Data, bobot, atau target dapat disesuaikan berdasarkan kesalahan sebelumnya
Fokus utama	Mengurangi variance melalui agregasi	Memperbaiki kesalahan secara bertahap
Kontribusi learner	Umumnya setara pada Bagging dasar	Dapat berbeda; misalnya pada AdaBoost melalui \(\alpha_m\)
Base learner	Sering menggunakan pohon yang relatif besar	Umumnya menggunakan learner sederhana/pohon dangkal
Komputasi	Mudah diparalelkan	Lebih sulit diparalelkan karena bersifat sequential
Parameter penting	Jumlah model/bootstrap	Jumlah iterasi, learning rate, dan kompleksitas learner

Tabel 1. Perbedaan Bagging dan Boosting

Dengan demikian, Bagging membangun banyak learner secara independen kemudian menggabungkan hasilnya, sedangkan Boosting membangun learner secara berurutan agar setiap learner baru membantu memperbaiki kesalahan yang masih tersisa.

2.3 Adaptive Boosting

AdaBoost (Adaptive Boosting) merupakan salah satu algoritma Boosting yang banyak digunakan untuk kasus klasifikasi. Pada klasifikasi berbasis pohon, base learner yang digunakan biasanya berupa pohon sederhana atau decision stump. Prinsip utama AdaBoost adalah memberikan perhatian lebih besar pada observasi yang masih salah diklasifikasikan.

Untuk klasifikasi biner, misalkan tersedia data latih \((x_i, y_i)\), \(i = 1,2,\dots,n\). Secara sederhana, proses AdaBoost dilakukan sebagai berikut.

1. Memberikan bobot awal yang sama

Pada awal proses, semua observasi dianggap sama penting sehingga diberi bobot yang sama, sebesar:

\[ w_i^{(1)} = \frac{1}{n}, \qquad i = 1,2,\dots,n. \]

dengan \(w_i^{(1)}\) merupakan bobot observasi ke-\(i\) pada iterasi pertama.

2. Membentuk weak learner

Pada setiap iterasi dibentuk weak learner \(G_m(x)\). Setelah prediksi dilakukan, dihitung kesalahan klasifikasi terbobot:

\[ err_m = \frac{\sum_{i=1}^{n} w_i^{(m)} \, I\{y_i \neq G_m(x_i)\}}{\sum_{i=1}^{n} w_i^{(m)}} \]

Keterangan:

• \(err_m\) = kesalahan klasifikasi terbobot pada learner ke-\(m\);
• \(w_i^{(m)}\) = bobot observasi ke-\(i\) pada iterasi ke-\(m\);
• \(G_m(x_i)\) = kelas hasil prediksi learner ke-\(m\) untuk observasi ke-\(i\);
• \(I\{y_i \neq G_m(x_i)\}\) = fungsi indikator, bernilai 1 apabila observasi ke-\(i\) salah diklasifikasikan dan 0 apabila diklasifikasikan dengan benar.

3. Memperbarui bobot

Observasi yang salah diklasifikasikan memperoleh bobot yang lebih besar sehingga learner berikutnya memberikan perhatian lebih besar pada observasi tersebut. Dengan demikian, setiap learner baru berusaha memperbaiki kesalahan yang masih dilakukan oleh learner sebelumnya.

4. Menggabungkan learner

Setiap learner diberi bobot berdasarkan performanya,

\[ \alpha_m = \log\left(\frac{1 - err_m}{err_m}\right), \]

dengan \(\alpha_m\) merupakan bobot atau tingkat kontribusi learner ke-\(m\) terhadap prediksi akhir. Kemudian prediksi akhir diperoleh melalui weighted voting:

\[ G(x) = \mathrm{sign}\left[\sum_{m=1}^{M} \alpha_m G_m(x)\right], \]

dengan \(M\) merupakan jumlah seluruh weak learner, sedangkan fungsi \(\mathrm{sign}(\cdot)\) menentukan kelas akhir berdasarkan tanda dari jumlah voting terbobot tersebut. Pada AdaBoost terdapat dua jenis bobot, yaitu bobot observasi yang mengatur perhatian terhadap data latih dan bobot learner yang mengatur kontribusi setiap learner terhadap prediksi akhir.

2.4 Metrik Evaluasi

Ukuran kebaikan model berasal dari confusion matrix, yaitu label yang menunjukkan perbandingan antara hasil prediksi dengan label aktual. Confusion Matrix dapat dilihat pada tabel di bawah ini (Syukron, Santoso, & Widiharih, 2020):

Predicted Class	True Class: Positive	True Class: Negative
Positive	True Positive Count (TP)	False Positive Count (FP)
Negative	False Negative Count (FN)	True Negative Count (TN)

Tabel 1. Confusion Matrix

1. Akurasi (Accuracy)

Akurasi merupakan metrik yang menunjukkan proporsi prediksi yang benar terhadap keseluruhan data. Akurasi dapat dihitung menggunakan formula berikut (Direktorat Jenderal Pendidikan Tinggi, Riset, dan Teknologi, n.d):

\[accuracy = \frac{TP + TN}{TP + FP + TN + FN}\]

Akurasi memang menjadi metrik yang paling umum untuk digunakan dalam menilai kinerja. Namun, metrik ini cenderung memberikan gambaran yang keliru ketika distribusi tidak seimbang. Pada data yang tidak seimbang (imbalanced dataset), sebuah model dapat terlihat berkinerja baik dengan akurasi tinggi, padahal tidak mampu mengenali kelas minoritas.

2. Sensitivitas

Sensitivitas merupakan persentase ketepatan prediksi pada kelas positif, artinya observasi di kelas positif pada data aktual diprediksi positif (true positive). Dimana persamaannya adalah (Istiana & Mustafiril, 2023):

\[Sensitivitas = \frac{TP}{TP + FN}\]

3. Spesifisitas

Spesifisitas adalah tingkat ketepatan pada kelas negatif dalam data aktual, yaitu persentase banyaknya prediksi yang tepat pada observasi yang sebenarnya negatif. Spesifisitas dapat dihitung menggunakan persamaan di bawah ini (Istiana & Mustafiril, 2023):

\[Spesifisitas = \frac{TN}{TN + FP}\] 4. Presisi

Metrik Presisi dapat digunakan untuk mengukur seberapa tepat model dalam memberikan prediksi positif. Nilai prediksi yang tinggi mengindikasikan bahwa model jarang salah memberikan label positif. Metrik presisi dapat dihitung menggunakan formula berikut (Direktorat Jenderal Pendidikan Tinggi, Riset, dan Teknologi, n.d):

\[Precision = \frac{TP}{TP + FP}\] 5. Recall

Recall merupakan metrik yang digunakan untuk mengukur kemampuan model dalam mengenali data yang sebenarnya termasuk dalam kelas positif. Semakin tinggi nilai recall, maka semakin besar proporsi data positif yang berhasil diidentifikasi dengan benar oleh model.

\[Recall = \frac{TP}{TP + FN}\] 6. F1-Score

F1-Score digunakan untuk melihat kinerja model dengan mempertimbangkan precision dan recall secara bersamaan. Nilai F1-Score diperoleh dari rata-rata harmonik kedua metrik tersebut, sehingga memberikan ukuran yang lebih seimbang. Perhitungan F1-Score dapat dilakukan menggunakan persamaan berikut (Direktorat Jenderal Pendidikan Tinggi, Riset, dan Teknologi, n.d):

\[F1\text{-}Score = 2 \times \frac{precision \times recall}{precision + recall}\]

BAB III METODE PENELITIAN

3.1 Jenis dan Sumber Data

Jenis data yang digunakan dalam praktikum ini terdiri atas data kategorik dan numerik. Data kategorik merupakan data yang menunjukkan kelompok atau kategori tertentu untuk membedakan karakteristik suatu objek, sedangkan data numerik merupakan data yang dinyatakan dalam bentuk angka dan dapat digunakan dalam perhitungan matematis. Sumber data yang digunakan dalam praktikum ini adalah data sekunder, yaitu data yang telah tersedia sebelumnya dan dimanfaatkan untuk memenuhi kebutuhan analisis. Data sekunder yang digunakan berupa dataset BreastCancer yang tersedia dalam pemrograman R dan terdiri atas 699 observasi serta 11 variabel.

3.2 Variabel Penelitian

Variabel penelitian merupakan karakteristik atau atribut yang menjadi objek pengamatan dalam suatu penelitian. Pada praktikum ini, dataset BreastCancer terdiri atas 11 variabel, yaitu satu variabel identitas, sembilan variabel prediktor, dan satu variabel target. Variabel Class berperan sebagai variabel target bertipe kategorik yang terdiri atas dua kelas, yaitu benign (jinak) dan malignant (ganas). Sembilan variabel prediktor meliputi Cl.thickness, Cell.size, Cell.shape, Marg.adhesion, Epith.c.size, Bare.nuclei, Bl.cromatin, Normal.nucleoli, dan Mitoses. Variabel Cl.thickness, Cell.size, Cell.shape, Marg.adhesion, dan Epith.c.size bertipe kategorik ordinal (ordered factor), sedangkan Bare.nuclei, Bl.cromatin, Normal.nucleoli, dan Mitoses bertipe kategorik (factor). Sementara itu, variabel Id bertipe karakter dan berfungsi sebagai identitas setiap pengamatan, sehingga tidak digunakan sebagai prediktor dalam pemodelan.

3.3 Analisis Data

Adapun analisis data pada batasan masalah ini sebagai berikut: 1. Memanggil dan mengeksplorasi dataset BreastCancer 2. Menghapus variabel Id dan menangani missing value. 3. Membagi data latih dan data uji dengan proporsi 70:30. 4. Membentuk model AdaBoost dengan decision stump sebagai weak learner dan jumlah learner dan jumlah learner 50, 100, dan 200. 5. Melakukan prediksi pada data uji dan menyusun confusion matrix serta menghitung metrik evaluasi (Accuracy, Precision, Recall, Specificity, dan F1-Score) 6. Membandingkan hasil evaluasi ketiga model dan perkembangan error pada data latih dan data uji.

BAB IV HASIL DAN PEMBAHASAN

4.1 Library dan Data

library(adabag)
library(mlbench)
library(caret)
library(rpart)

# Memuat data
data(BreastCancer, package = "mlbench")
bc <- BreastCancer

# Melihat struktur dan ringkasan data
str(bc)

## 'data.frame':    699 obs. of  11 variables:
##  $ Id             : chr  "1000025" "1002945" "1015425" "1016277" ...
##  $ Cl.thickness   : Ord.factor w/ 10 levels "1"<"2"<"3"<"4"<..: 5 5 3 6 4 8 1 2 2 4 ...
##  $ Cell.size      : Ord.factor w/ 10 levels "1"<"2"<"3"<"4"<..: 1 4 1 8 1 10 1 1 1 2 ...
##  $ Cell.shape     : Ord.factor w/ 10 levels "1"<"2"<"3"<"4"<..: 1 4 1 8 1 10 1 2 1 1 ...
##  $ Marg.adhesion  : Ord.factor w/ 10 levels "1"<"2"<"3"<"4"<..: 1 5 1 1 3 8 1 1 1 1 ...
##  $ Epith.c.size   : Ord.factor w/ 10 levels "1"<"2"<"3"<"4"<..: 2 7 2 3 2 7 2 2 2 2 ...
##  $ Bare.nuclei    : Factor w/ 10 levels "1","2","3","4",..: 1 10 2 4 1 10 10 1 1 1 ...
##  $ Bl.cromatin    : Factor w/ 10 levels "1","2","3","4",..: 3 3 3 3 3 9 3 3 1 2 ...
##  $ Normal.nucleoli: Factor w/ 10 levels "1","2","3","4",..: 1 2 1 7 1 7 1 1 1 1 ...
##  $ Mitoses        : Factor w/ 9 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 5 1 ...
##  $ Class          : Factor w/ 2 levels "benign","malignant": 1 1 1 1 1 2 1 1 1 1 ...

summary(bc)

##          Id       Cl.thickness   Cell.size     Cell.shape  Marg.adhesion
##  Length   :699   1      :145   1      :384   1      :353   1      :407  
##  N.unique :645   5      :130   10     : 67   2      : 59   2      : 58  
##  N.blank  :  0   3      :108   3      : 52   10     : 58   3      : 58  
##  Min.nchar:  5   4      : 80   2      : 45   3      : 56   10     : 55  
##  Max.nchar:  8   10     : 69   4      : 40   4      : 44   4      : 33  
##                  2      : 50   5      : 30   5      : 34   8      : 25  
##                  (Other):117   (Other): 81   (Other): 95   (Other): 63  
##   Epith.c.size  Bare.nuclei   Bl.cromatin  Normal.nucleoli    Mitoses   
##  2      :386   1      :402   2      :166   1      :443     1      :579  
##  3      : 72   10     :132   3      :165   10     : 61     2      : 35  
##  4      : 48   2      : 30   1      :152   3      : 44     3      : 33  
##  1      : 47   5      : 30   7      : 73   2      : 36     10     : 14  
##  6      : 41   3      : 28   4      : 40   8      : 24     4      : 12  
##  5      : 39   (Other): 61   5      : 34   6      : 22     7      :  9  
##  (Other): 66   NAs    : 16   (Other): 69   (Other): 69     (Other): 17  
##        Class    
##  benign   :458  
##  malignant:241  
##                 
##                 
##                 
##                 
## 

Pada tahap awal penyelesaian batasan masalah, dilakukan pemanggilan dan pemeriksaan dataset BreastCancer dari paket mlbench. Setelah melakukan pemeriksaan struktur, dataset terdiri atas 699 observasi dan 11 variabel. Variabel tersebut mencakup satu variabel identitas, yaitu Id, sembilan variabel prediktor yang menggambarkan karakteristik sel, serta satu variabel respons Class yang terdiri atas dua kategori, yaitu benign dan malignant. Hasil fungsi summary() menunjukkan bahwa terdapat 458 observasi pada kelas benign dan 241 observasi pada kelas malignant. Selain itu, ditemukan 16 nilai hilang (missing values) pada variabel Bare.nuclei. Oleh karena itu, diperlukan penanganan nilai hilang sebelum pemodelan dilakukan. Variabel Id berfungsi sebagai identitas observasi, sehingga pada kasus ini variabel Id dihapus.

4.2 Persiapan Data

# Menghapus Id karena hanya merupakan identifier
bc$Id <- NULL

# Menghapus observasi yang mengandung missing value
bc <- na.omit(bc)

# Memastikan respons berupa factor
bc$Class <- droplevels(as.factor(bc$Class))

# Melihat kategori respons
levels(bc$Class)

## [1] "benign"    "malignant"

# Distribusi kelas
table(bc$Class)

## 
##    benign malignant 
##       444       239

prop.table(table(bc$Class))

## 
##    benign malignant 
## 0.6500732 0.3499268

Tahap selanjutnya adalah eksplorasi data. Pada tahapan ini dilakukan untuk mengetahui struktur, ukuran, dan karakteristik dataset BreastCancer. Berdasarkan hasil, dataset terdiri atas 699 pengamatan dan 11 variabel. Hasil ringkasan data menunjukkan bahwa variabel Bare.nuclei memiliki 16 missing value, sehingga perlu dilakukan penanganan sebelum pemodelan. Selain itu, variabel Id memiliki 645 nilai unik dan berfungsi sebagai identitas pengamatan, bukan sebagai prediktor dalam model. Kemudian, dilakukan pemeriksaan distribusi kelas pada variabel Class. Hasilnya menunjukkan bahwa terdapat 458 pengamatan pada kelas benign dan 241 pengamatan pada kelas malignant. Distribusi tersebut menunjukkan bahwa jumlah pengamatan pada kelas benign lebih banyak dibandingkan kelas malignant, sehingga data memiliki distribusi kelas yang tidak seimbang.

4.3 Penanganan Missing Value

# Membagi data training dan testing (70% training dan 30% testing)
set.seed(234)
idx.cls <- createDataPartition(bc$Class, p = 0.70, list = FALSE)
bc.train <- bc[idx.cls, ]
bc.test <- bc[-idx.cls, ]

# Memeriksa ukuran data
dim(bc.train)

## [1] 479  10

dim(bc.test)

## [1] 204  10

# Distribusi kelas pada training dan testing
table(bc.train$Class)

## 
##    benign malignant 
##       311       168

table(bc.test$Class)

## 
##    benign malignant 
##       133        71

prop.table(table(bc.train$Class))

## 
##    benign malignant 
## 0.6492693 0.3507307

prop.table(table(bc.test$Class))

## 
##    benign malignant 
## 0.6519608 0.3480392

Data dibagi menjadi data training sebesar 70% dan data testing sebesar 30% menggunakan createDataPartition() dengan set.seed(234). Hasil pembagian menghasilkan 479 observasi untuk training dan 204 observasi untuk testing, masing-masing dengan 10 variabel. Proporsi kelas pada kedua data relatif sama, yaitu sekitar 65% benign dan 35% malignant.

4.4 Membentuk model AdaBoost

# Membentuk model AdaBoost dengan decision stump (maxdepth = 1)
# Model 1: 50 learner
set.seed(234)
boost.ada50 <- boosting(Class ~ ., data = bc.train, boos = FALSE, mfinal = 50,
                        coeflearn = "Freund",
                        control = rpart.control(maxdepth = 1, minsplit = 10, cp = 0))
boost.ada50

## $formula
## Class ~ .
## 
## $trees
## $trees[[1]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.35073070 benign (0.77410861 0.22589139)  
##   2) Cell.size=1,2 291 0.01461378 benign (0.98686034 0.01313966) *
##   3) Cell.size=3,4,5,6,7,8,9,10 188 0.05636743 malignant (0.23690224 0.76309776) *
## 
## $trees[[2]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.28384010 benign (0.8642427 0.1357573)  
##   2) Bare.nuclei=1,2,3,4 336 0.04044944 benign (0.9754087 0.0245913) *
##   3) Bare.nuclei=5,6,7,8,9,10 143 0.08027098 malignant (0.4541872 0.5458128) *
## 
## $trees[[3]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.30593700 benign (0.83731267 0.16268733)  
##   2) Bare.nuclei=1,5,7,8 321 0.09480714 benign (0.93600088 0.06399912) *
##   3) Bare.nuclei=2,3,4,6,9,10 158 0.08287293 malignant (0.47103720 0.52896280) *
## 
## $trees[[4]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.3951662 benign (0.7008386 0.2991614)  
##   2) Cell.size=1,2,3,4,5,6,7,8,9 430 0.2676721 benign (0.7757099 0.2242901) *
##   3) Cell.size=10 49 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[5]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4129529 malignant (0.3310271 0.6689729)  
##   2) Marg.adhesion=1,2 317 0.1051429 benign (0.6373274 0.3626726) *
##   3) Marg.adhesion=3,4,5,6,7,8,9,10 162 0.1502891 malignant (0.1799101 0.8200899) *
## 
## $trees[[6]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4705728 malignant (0.4413488 0.5586512)  
##   2) Cl.thickness=1,2,3,4,5,6,7,8 418 0.3588866 benign (0.5382002 0.4617998) *
##   3) Cl.thickness=9,10 61 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[7]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.3669965 malignant (0.2515718 0.7484282) *
## 
## $trees[[8]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.50000000 malignant (0.5000000 0.5000000)  
##   2) Normal.nucleoli=1,2,3,5,7,8 403 0.27951890 benign (0.6146659 0.3853341) *
##   3) Normal.nucleoli=4,6,9,10 76 0.05412526 malignant (0.1971013 0.8028987) *
## 
## $trees[[9]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.41567430 malignant (0.3360129 0.6639871)  
##   2) Bare.nuclei=2,3,5,7,8 89 0.26110990 benign (0.4744262 0.5255738) *
##   3) Bare.nuclei=1,4,6,9,10 390 0.08434473 malignant (0.1565713 0.8434287) *
## 
## $trees[[10]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.37517690 malignant (0.2649998 0.7350002)  
##   2) Cl.thickness=1,2,3 207 0.05427183 benign (0.6200856 0.3799144) *
##   3) Cl.thickness=4,5,6,7,8,9,10 272 0.22765340 malignant (0.1932780 0.8067220) *
## 
## $trees[[11]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.49353060 benign (0.5129366 0.4870634)  
##   2) Normal.nucleoli=2,7,8 52 0.05445584 benign (0.7985195 0.2014805) *
##   3) Normal.nucleoli=1,3,4,5,6,9,10 427 0.29616030 malignant (0.4090510 0.5909490) *
## 
## $trees[[12]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4157276 benign (0.6638891 0.3361109)  
##   2) Cl.thickness=1,2,3,4,5,6,7,8 418 0.3235771 benign (0.7173322 0.2826678) *
##   3) Cl.thickness=9,10 61 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[13]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4318839 malignant (0.3662502 0.6337498) *
## 
## $trees[[14]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.500000000 benign (0.50000000 0.50000000)  
##   2) Marg.adhesion=1,2,3,4,5,6 412 0.385276100 benign (0.56233109 0.43766891) *
##   3) Marg.adhesion=7,8,9,10 67 0.004984869 malignant (0.04164164 0.95835836) *
## 
## $trees[[15]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.412310400 malignant (0.32985405 0.67014595)  
##   2) Mitoses=1,2 418 0.410805200 malignant (0.37204469 0.62795531) *
##   3) Mitoses=3,4,5,6,7,8,10 61 0.001505178 malignant (0.01032388 0.98967612) *
## 
## $trees[[16]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.500000000 benign (0.5000000 0.5000000)  
##   2) Mitoses=1,2 418 0.413873600 benign (0.5462153 0.4537847) *
##   3) Mitoses=3,4,5,6,7,8,10 61 0.001825297 malignant (0.0207534 0.9792466) *
## 
## $trees[[17]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4284951000 malignant (0.359856375 0.640143625)  
##   2) Bl.cromatin=1 100 0.0002938377 benign (0.992079603 0.007920397) *
##   3) Bl.cromatin=2,3,4,5,6,7,8,9,10 379 0.3794064000 malignant (0.332445620 0.667554380) *
## 
## $trees[[18]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4608184 benign (0.5778849 0.4221151)  
##   2) Bare.nuclei=1,2,3,4,5,7,8 379 0.3954907 benign (0.6146671 0.3853329) *
##   3) Bare.nuclei=6,9,10 100 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[19]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.44596630 malignant (0.39318014 0.60681986)  
##   2) Bl.cromatin=1,2,3,4,7 411 0.43954650 malignant (0.43081988 0.56918012) *
##   3) Bl.cromatin=5,6,8,9,10 68 0.00641977 malignant (0.05631451 0.94368549) *
## 
## $trees[[20]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.500000000 malignant (0.50000000 0.50000000)  
##   2) Bl.cromatin=1,2,3,4,7 411 0.421850500 benign (0.53878624 0.46121376) *
##   3) Bl.cromatin=5,6,8,9,10 68 0.007197595 malignant (0.08433323 0.91566677) *
## 
## $trees[[21]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.43995 malignant (0.3816076 0.6183924) *
## 
## $trees[[22]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.500000000 benign (0.50000000 0.50000000)  
##   2) Marg.adhesion=1,2,3,4,5,6 412 0.423221100 benign (0.53754739 0.46245261) *
##   3) Marg.adhesion=7,8,9,10 67 0.008054692 malignant (0.09494695 0.90505305) *
## 
## $trees[[23]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4418372 malignant (0.3852275 0.6147725)  
##   2) Normal.nucleoli=2,3,4,7 84 0.1547312 benign (0.5325152 0.4674848) *
##   3) Normal.nucleoli=1,5,6,8,9,10 395 0.2191779 malignant (0.3007283 0.6992717) *
## 
## $trees[[24]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.470906900 malignant (0.44201016 0.55798984)  
##   2) Mitoses=1,2 418 0.447787100 benign (0.48272211 0.51727789) *
##   3) Mitoses=3,4,5,6,7,8,10 61 0.001400099 malignant (0.01509502 0.98490498) *
## 
## $trees[[25]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4277530 malignant (0.3584612 0.6415388)  
##   2) Cell.shape=1,2 286 0.0930425 benign (0.5697126 0.4302874) *
##   3) Cell.shape=3,4,5,6,7,8,9,10 193 0.2629486 malignant (0.2908636 0.7091364) *
## 
## $trees[[26]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.497271100 malignant (0.49454229 0.50545771)  
##   2) Epith.c.size=2,3,4,5,6,7,8,9,10 448 0.409440900 benign (0.54194665 0.45805335) *
##   3) Epith.c.size=1 31 0.007523409 malignant (0.07387815 0.92612185) *
## 
## $trees[[27]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4290196 malignant (0.3608437 0.6391563)  
##   2) Cl.thickness=1,2,3,4,5,6,7,8 418 0.4290196 malignant (0.3926055 0.6073945) *
##   3) Cl.thickness=9,10 61 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[28]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 malignant (0.5000000 0.5000000)  
##   2) Cl.thickness=1,2,3,4,5,6,7,8 418 0.4367134 benign (0.5337812 0.4662188) *
##   3) Cl.thickness=9,10 61 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[29]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.443823800 malignant (0.38904813 0.61095187)  
##   2) Mitoses=1,2 418 0.442614000 malignant (0.42081376 0.57918624) *
##   3) Mitoses=3,4,5,6,7,8,10 61 0.001209749 malignant (0.01359437 0.98640563) *
## 
## $trees[[30]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.500000000 malignant (0.50000000 0.50000000)  
##   2) Mitoses=1,2 418 0.437028000 benign (0.53292265 0.46707735) *
##   3) Mitoses=3,4,5,6,7,8,10 61 0.001362871 malignant (0.02118401 0.97881599) *
## 
## $trees[[31]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4454905 malignant (0.3922615 0.6077385) *
## 
## $trees[[32]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Epith.c.size=2,3,4,5,6,7,8,9,10 448 0.4258433 benign (0.5346919 0.4653081) *
##   3) Epith.c.size=1 31 0.0106576 malignant (0.1256580 0.8743420) *
## 
## $trees[[33]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4464078 malignant (0.3940330 0.6059670) *
## 
## $trees[[34]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 malignant (0.5000000 0.5000000)  
##   2) Cl.thickness=1,2,3,4,5,6,7,8 418 0.4496101 benign (0.5265319 0.4734681) *
##   3) Cl.thickness=9,10 61 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[35]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.454223500 malignant (0.40920794 0.59079206)  
##   2) Marg.adhesion=1,2,3,4,5,6 412 0.446288200 malignant (0.44281177 0.55718823) *
##   3) Marg.adhesion=7,8,9,10 67 0.007935268 malignant (0.07767819 0.92232181) *
## 
## $trees[[36]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.500000000 benign (0.5000000 0.5000000)  
##   2) Marg.adhesion=1,2,3,4,5,6 412 0.428161900 benign (0.5343165 0.4656835) *
##   3) Marg.adhesion=7,8,9,10 67 0.008734983 malignant (0.1084107 0.8915893) *
## 
## $trees[[37]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.44620890 malignant (0.3936487 0.6063513)  
##   2) Bare.nuclei=1,2,3,5,7 354 0.34702260 benign (0.4586714 0.5413286) *
##   3) Bare.nuclei=4,6,8,9,10 125 0.08128174 malignant (0.2405478 0.7594522) *
## 
## $trees[[38]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.41405 malignant (0.3330337 0.6669663) *
## 
## $trees[[39]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.500000000 malignant (0.50000000 0.50000000)  
##   2) Mitoses=1,2 418 0.446919300 benign (0.52712270 0.47287730) *
##   3) Mitoses=3,4,5,6,7,8,10 61 0.001813078 malignant (0.03302882 0.96697118) *
## 
## $trees[[40]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4538760 malignant (0.4085303 0.5914697)  
##   2) Normal.nucleoli=2,4,7,8 66 0.1176017 benign (0.5659454 0.4340546) *
##   3) Normal.nucleoli=1,3,5,6,9,10 413 0.2693754 malignant (0.3431565 0.6568435) *
## 
## $trees[[41]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4985351 malignant (0.4970702 0.5029298)  
##   2) Cl.thickness=1,2,3,4,5,6 371 0.3201975 benign (0.5759397 0.4240603) *
##   3) Cl.thickness=7,8,9,10 108 0.0611014 malignant (0.2509978 0.7490022) *
## 
## $trees[[42]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4336324000 malignant (0.369562990 0.630437010)  
##   2) Bl.cromatin=1 100 0.0001767088 benign (0.992607889 0.007392111) *
##   3) Bl.cromatin=2,3,4,5,6,7,8,9,10 379 0.4026408000 malignant (0.352531012 0.647468988) *
## 
## $trees[[43]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4742712 benign (0.5513218 0.4486782)  
##   2) Epith.c.size=2,3,4,5,6,7,8,9,10 448 0.4053968 benign (0.5833990 0.4166010) *
##   3) Epith.c.size=1 31 0.0135865 malignant (0.1794319 0.8205681) *
## 
## $trees[[44]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4569431 malignant (0.4145201 0.5854799)  
##   2) Marg.adhesion=1,2,3,4,5 398 0.4316513 malignant (0.4528515 0.5471485) *
##   3) Marg.adhesion=6,7,8,9,10 81 0.0252918 malignant (0.1695645 0.8304355) *
## 
## $trees[[45]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 5.000000e-01 benign (0.500000000 0.500000000)  
##   2) Epith.c.size=1,2,3,4,5,6,7 439 4.584224e-01 benign (0.521658880 0.478341120) *
##   3) Epith.c.size=8,9,10 40 6.367098e-05 malignant (0.001529035 0.998470965) *
## 
## $trees[[46]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4616793 malignant (0.4238061 0.5761939)  
##   2) Bare.nuclei=1,2,3,5,7 354 0.3570172 benign (0.4820857 0.5179143) *
##   3) Bare.nuclei=4,6,8,9,10 125 0.0741931 malignant (0.2597852 0.7402148) *
## 
## $trees[[47]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4266523 malignant (0.3563950 0.6436050) *
## 
## $trees[[48]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.50000000 benign (0.5000000 0.5000000)  
##   2) Marg.adhesion=1,2,3,4,5,6 412 0.42870390 benign (0.5327921 0.4672079) *
##   3) Marg.adhesion=7,8,9,10 67 0.01111687 malignant (0.1348922 0.8651078) *
## 
## $trees[[49]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4490011 malignant (0.3990524 0.6009476) *
## 
## $trees[[50]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.500000000 malignant (0.50000000 0.50000000)  
##   2) Mitoses=1,2 418 0.453915600 benign (0.52335752 0.47664248) *
##   3) Mitoses=3,4,5,6,7,8,10 61 0.001596736 malignant (0.03348782 0.96651218) *
## 
## 
## $weights
##  [1] 2.5717138 1.9856256 1.5321449 1.0064655 1.0698480 0.5801999 0.5451234
##  [8] 0.6917486 0.6390794 0.9349320 0.6163319 0.7373809 0.2741688 0.4462154
## [15] 0.3544226 0.3404553 0.4908206 0.4242897 0.2169821 0.2857359 0.2413651
## [22] 0.2766479 0.5154829 0.2039552 0.5928070 0.3352479 0.2858522 0.2545115
## [29] 0.2256576 0.2476953 0.2189081 0.2553753 0.2151954 0.2022461 0.1836203
## [36] 0.2537656 0.2887726 0.3472479 0.2057939 0.4600370 0.4840385 0.3937390
## [43] 0.3269484 0.1726552 0.1664390 0.2769147 0.2955228 0.2418894 0.2047074
## [50] 0.1784223
## 
## $votes
##             [,1]      [,2]
##   [1,] 16.463294  7.831852
##   [2,] 16.505160  7.789985
##   [3,] 12.792793 11.502353
##   [4,]  6.318271 17.976874
##   [5,] 17.398226  6.896920
##   [6,] 16.463294  7.831852
##   [7,] 16.480654  7.814491
##   [8,] 11.619631 12.675515
##   [9,] 16.505160  7.789985
##  [10,]  4.556563 19.738583
##  [11,] 12.260370 12.034776
##  [12,] 16.463294  7.831852
##  [13,] 16.463294  7.831852
##  [14,] 16.463294  7.831852
##  [15,]  5.433141 18.862004
##  [16,]  7.932253 16.362892
##  [17,] 17.398226  6.896920
##  [18,] 17.398226  6.896920
##  [19,] 12.762344 11.532802
##  [20,] 16.463294  7.831852
##  [21,] 17.398226  6.896920
##  [22,] 16.480654  7.814491
##  [23,]  7.565013 16.730133
##  [24,] 17.398226  6.896920
##  [25,] 17.398226  6.896920
##  [26,] 17.398226  6.896920
##  [27,] 15.545722  8.749423
##  [28,] 11.817311 12.477835
##  [29,]  9.861595 14.433551
##  [30,]  6.957282 17.337864
##  [31,] 12.062486 12.232659
##  [32,]  7.820159 16.474987
##  [33,] 15.393446  8.901700
##  [34,] 11.978870 12.316276
##  [35,]  9.002102 15.293044
##  [36,]  8.270896 16.024250
##  [37,]  7.891876 16.403270
##  [38,]  7.932253 16.362892
##  [39,] 11.077004 13.218142
##  [40,]  9.861595 14.433551
##  [41,] 10.644111 13.651035
##  [42,] 16.505160  7.789985
##  [43,]  8.470741 15.824405
##  [44,] 11.713959 12.581187
##  [45,] 10.079299 14.215847
##  [46,] 16.463294  7.831852
##  [47,]  7.932838 16.362308
##  [48,]  9.437291 14.857855
##  [49,] 15.825557  8.469589
##  [50,]  5.320651 18.974494
##  [51,] 16.805419  7.489727
##  [52,] 13.891580 10.403566
##  [53,] 16.505160  7.789985
##  [54,] 16.480654  7.814491
##  [55,] 16.463294  7.831852
##  [56,] 16.463294  7.831852
##  [57,] 16.505160  7.789985
##  [58,]  8.159966 16.135180
##  [59,] 11.535395 12.759751
##  [60,] 16.463294  7.831852
##  [61,] 17.398226  6.896920
##  [62,] 17.398226  6.896920
##  [63,] 18.282786  6.012360
##  [64,] 17.398226  6.896920
##  [65,] 18.282786  6.012360
##  [66,] 16.463294  7.831852
##  [67,]  4.498139 19.797007
##  [68,]  8.835663 15.459483
##  [69,]  7.193437 17.101709
##  [70,] 11.267351 13.027795
##  [71,]  8.577268 15.717877
##  [72,] 17.913709  6.381437
##  [73,]  7.621225 16.673921
##  [74,] 14.163194 10.131952
##  [75,]  6.599674 17.695471
##  [76,] 13.933447 10.361699
##  [77,] 16.936239  7.358906
##  [78,] 13.513521 10.781625
##  [79,] 16.505160  7.789985
##  [80,] 15.570229  8.724917
##  [81,]  4.413169 19.881976
##  [82,] 17.398226  6.896920
##  [83,]  4.842299 19.452847
##  [84,]  7.429950 16.865196
##  [85,] 18.116347  6.178799
##  [86,] 15.870487  8.424659
##  [87,]  3.461685 20.833461
##  [88,] 18.990078  5.305068
##  [89,] 16.085711  8.209434
##  [90,] 16.463294  7.831852
##  [91,] 18.282786  6.012360
##  [92,] 18.282786  6.012360
##  [93,] 18.282786  6.012360
##  [94,]  7.963163 16.331983
##  [95,] 16.936239  7.358906
##  [96,] 17.398226  6.896920
##  [97,] 12.321472 11.973674
##  [98,]  6.970747 17.324399
##  [99,] 16.480654  7.814491
## [100,]  9.700414 14.594731
## [101,] 16.454788  7.840358
## [102,] 18.282786  6.012360
## [103,]  7.721178 16.573968
## [104,] 17.398226  6.896920
## [105,] 17.398226  6.896920
## [106,]  3.594730 20.700416
## [107,]  6.768917 17.526229
## [108,] 18.055146  6.240000
## [109,] 17.398226  6.896920
## [110,] 15.295827  8.999319
## [111,] 17.162080  7.133065
## [112,]  4.468150 19.826996
## [113,] 17.398226  6.896920
## [114,] 17.365214  6.929932
## [115,] 18.282786  6.012360
## [116,] 17.398226  6.896920
## [117,] 17.398226  6.896920
## [118,]  6.951403 17.343743
## [119,] 17.398226  6.896920
## [120,]  8.902214 15.392931
## [121,] 16.463294  7.831852
## [122,] 16.480654  7.814491
## [123,] 18.282786  6.012360
## [124,]  7.579028 16.716117
## [125,]  6.951403 17.343743
## [126,]  4.659812 19.635333
## [127,] 16.805419  7.489727
## [128,] 16.463294  7.831852
## [129,] 17.398226  6.896920
## [130,] 17.398226  6.896920
## [131,] 11.474709 12.820436
## [132,] 15.393446  8.901700
## [133,] 18.282786  6.012360
## [134,] 17.398226  6.896920
## [135,]  5.362848 18.932298
## [136,] 17.398226  6.896920
## [137,] 17.398226  6.896920
## [138,]  3.628124 20.667022
## [139,]  7.617537 16.677609
## [140,] 15.545722  8.749423
## [141,]  2.858349 21.436797
## [142,] 17.398226  6.896920
## [143,]  1.369746 22.925399
## [144,]  2.260332 22.034813
## [145,]  8.202338 16.092808
## [146,] 17.398226  6.896920
## [147,]  6.928625 17.366521
## [148,] 17.398226  6.896920
## [149,] 13.129328 11.165818
## [150,] 10.116425 14.178721
## [151,]  4.771052 19.524094
## [152,] 17.398226  6.896920
## [153,]  4.485316 19.809830
## [154,] 11.990192 12.304953
## [155,]  3.312412 20.982734
## [156,]  9.706592 14.588554
## [157,] 12.260370 12.034776
## [158,] 14.134764 10.160382
## [159,]  6.266257 18.028889
## [160,]  7.209412 17.085734
## [161,]  6.762396 17.532750
## [162,] 14.423056  9.872089
## [163,] 15.765944  8.529202
## [164,] 15.570229  8.724917
## [165,]  6.088524 18.206621
## [166,]  8.822470 15.472676
## [167,] 15.771545  8.523600
## [168,] 18.282786  6.012360
## [169,]  6.957282 17.337864
## [170,]  8.470741 15.824405
## [171,] 17.398226  6.896920
## [172,] 17.398226  6.896920
## [173,] 11.670311 12.624835
## [174,]  2.782225 21.512921
## [175,]  8.313508 15.981638
## [176,]  3.958547 20.336599
## [177,]  8.520289 15.774857
## [178,] 17.462339  6.832807
## [179,]  9.725958 14.569188
## [180,]  9.072358 15.222788
## [181,] 17.398226  6.896920
## [182,]  9.725958 14.569188
## [183,] 18.990078  5.305068
## [184,] 17.398226  6.896920
## [185,] 17.398226  6.896920
## [186,] 17.398226  6.896920
## [187,]  7.963163 16.331983
## [188,] 17.398226  6.896920
## [189,]  6.460107 17.835039
## [190,]  6.762396 17.532750
## [191,]  7.094990 17.200156
## [192,]  4.975511 19.319635
## [193,]  1.203308 23.091838
## [194,]  4.634589 19.660557
## [195,] 17.398226  6.896920
## [196,]  5.978284 18.316862
## [197,]  6.786431 17.508715
## [198,] 12.474231 11.820915
## [199,] 16.863815  7.431331
## [200,] 10.219173 14.075973
## [201,]  8.955306 15.339840
## [202,] 17.398226  6.896920
## [203,]  1.203308 23.091838
## [204,] 17.398226  6.896920
## [205,]  6.534104 17.761042
## [206,]  3.594730 20.700416
## [207,] 17.398226  6.896920
## [208,] 15.765944  8.529202
## [209,] 17.398226  6.896920
## [210,] 18.282786  6.012360
## [211,] 10.359930 13.935216
## [212,] 16.480654  7.814491
## [213,]  9.306509 14.988637
## [214,]  8.120689 16.174457
## [215,] 17.398226  6.896920
## [216,] 11.397862 12.897284
## [217,]  7.414437 16.880709
## [218,] 17.398226  6.896920
## [219,]  6.502660 17.792486
## [220,] 17.398226  6.896920
## [221,] 17.913709  6.381437
## [222,]  9.695205 14.599941
## [223,]  9.018688 15.276457
## [224,]  5.462801 18.832345
## [225,] 16.463294  7.831852
## [226,] 18.055146  6.240000
## [227,]  8.236661 16.058485
## [228,]  7.520910 16.774235
## [229,] 18.282786  6.012360
## [230,] 17.398226  6.896920
## [231,] 16.480654  7.814491
## [232,]  7.752622 16.542524
## [233,]  5.840900 18.454246
## [234,] 17.398226  6.896920
## [235,] 18.282786  6.012360
## [236,]  4.390124 19.905022
## [237,] 17.880697  6.414449
## [238,] 12.460822 11.834324
## [239,] 12.196657 12.098489
## [240,] 16.278006  8.017140
## [241,] 11.579043 12.716103
## [242,]  5.569521 18.725625
## [243,] 17.398226  6.896920
## [244,] 11.744869 12.550277
## [245,]  4.184878 20.110268
## [246,]  7.447680 16.847466
## [247,]  3.175949 21.119197
## [248,]  4.969068 19.326078
## [249,] 16.505160  7.789985
## [250,] 12.405708 11.889438
## [251,] 17.398226  6.896920
## [252,] 17.398226  6.896920
## [253,]  3.461685 20.833461
## [254,]  4.348853 19.946293
## [255,] 15.887847  8.407298
## [256,] 17.689979  6.605167
## [257,] 16.463294  7.831852
## [258,] 16.463294  7.831852
## [259,] 17.398226  6.896920
## [260,] 18.282786  6.012360
## [261,] 17.398226  6.896920
## [262,] 13.298773 10.996372
## [263,] 18.282786  6.012360
## [264,]  5.362848 18.932298
## [265,] 18.282786  6.012360
## [266,] 16.126602  8.168544
## [267,]  7.237139 17.058007
## [268,] 13.298773 10.996372
## [269,] 18.990078  5.305068
## [270,] 17.162080  7.133065
## [271,] 17.398226  6.896920
## [272,]  4.771052 19.524094
## [273,] 17.398226  6.896920
## [274,] 17.365214  6.929932
## [275,] 16.805419  7.489727
## [276,] 17.347854  6.947292
## [277,] 18.990078  5.305068
## [278,]  5.484551 18.810595
## [279,] 18.282786  6.012360
## [280,] 18.804789  5.490356
## [281,] 17.398226  6.896920
## [282,] 16.463294  7.831852
## [283,] 13.933447 10.361699
## [284,] 17.398226  6.896920
## [285,] 17.398226  6.896920
## [286,]  6.414076 17.881070
## [287,] 12.863599 11.431547
## [288,]  8.662552 15.632594
## [289,] 17.398226  6.896920
## [290,] 15.711072  8.584074
## [291,] 15.435312  8.859833
## [292,] 13.814256 10.480890
## [293,] 15.870487  8.424659
## [294,]  2.093894 22.201252
## [295,] 17.398226  6.896920
## [296,] 17.398226  6.896920
## [297,] 16.418364  7.876782
## [298,] 16.985298  7.309848
## [299,] 18.055146  6.240000
## [300,] 17.212938  7.082208
## [301,] 11.908544 12.386602
## [302,]  6.246913 18.048233
## [303,] 11.110300 13.184846
## [304,] 17.347854  6.947292
## [305,] 17.347854  6.947292
## [306,] 14.180173 10.114972
## [307,] 16.319872  7.975274
## [308,] 17.389720  6.905426
## [309,] 15.393446  8.901700
## [310,] 18.282786  6.012360
## [311,] 18.282786  6.012360
## [312,] 17.347854  6.947292
## [313,] 17.365214  6.929932
## [314,]  5.525175 18.769971
## [315,] 14.475874  9.819271
## [316,] 17.212938  7.082208
## [317,]  3.266798 21.028348
## [318,] 16.936239  7.358906
## [319,] 16.278006  8.017140
## [320,] 18.939705  5.355440
## [321,] 17.347854  6.947292
## [322,]  7.838765 16.456381
## [323,] 17.347854  6.947292
## [324,] 17.398226  6.896920
## [325,] 16.430282  7.864864
## [326,] 17.347854  6.947292
## [327,] 17.347854  6.947292
## [328,] 18.282786  6.012360
## [329,] 17.347854  6.947292
## [330,] 17.347854  6.947292
## [331,]  6.502660 17.792486
## [332,] 16.430282  7.864864
## [333,]  4.078773 20.216373
## [334,]  6.259655 18.035491
## [335,] 15.402300  8.892845
## [336,] 16.480654  7.814491
## [337,]  1.775759 22.519387
## [338,] 11.336045 12.959101
## [339,] 11.793748 12.501398
## [340,] 18.282786  6.012360
## [341,]  7.721178 16.573968
## [342,] 15.545722  8.749423
## [343,]  5.210458 19.084688
## [344,] 12.828382 11.466764
## [345,] 16.480654  7.814491
## [346,] 16.463294  7.831852
## [347,] 16.463294  7.831852
## [348,] 16.463294  7.831852
## [349,] 16.463294  7.831852
## [350,] 16.463294  7.831852
## [351,] 18.282786  6.012360
## [352,]  8.073404 16.221742
## [353,] 16.184953  8.110192
## [354,] 17.347854  6.947292
## [355,] 18.282786  6.012360
## [356,] 16.463294  7.831852
## [357,] 16.480654  7.814491
## [358,]  7.435442 16.859704
## [359,] 18.282786  6.012360
## [360,]  6.248368 18.046778
## [361,] 18.282786  6.012360
## [362,] 17.347854  6.947292
## [363,] 16.463294  7.831852
## [364,] 16.755047  7.540099
## [365,] 15.545722  8.749423
## [366,] 17.398226  6.896920
## [367,] 16.805419  7.489727
## [368,] 16.463294  7.831852
## [369,] 16.463294  7.831852
## [370,] 16.463294  7.831852
## [371,] 16.463294  7.831852
## [372,] 15.570229  8.724917
## [373,] 18.282786  6.012360
## [374,] 14.776140  9.519006
## [375,] 16.463294  7.831852
## [376,] 16.805419  7.489727
## [377,] 17.365214  6.929932
## [378,] 17.365214  6.929932
## [379,] 17.398226  6.896920
## [380,] 17.398226  6.896920
## [381,] 18.990078  5.305068
## [382,] 14.967949  9.327197
## [383,] 17.398226  6.896920
## [384,] 16.463294  7.831852
## [385,] 16.463294  7.831852
## [386,] 17.398226  6.896920
## [387,]  5.525175 18.769971
## [388,]  8.306987 15.988159
## [389,]  6.628765 17.666380
## [390,]  7.370351 16.924795
## [391,]  6.259655 18.035491
## [392,] 17.398226  6.896920
## [393,] 10.669335 13.625811
## [394,] 16.463294  7.831852
## [395,] 17.398226  6.896920
## [396,] 17.398226  6.896920
## [397,]  7.721178 16.573968
## [398,]  5.737228 18.557918
## [399,] 18.282786  6.012360
## [400,] 16.278006  8.017140
## [401,] 18.282786  6.012360
## [402,]  7.858965 16.436181
## [403,] 17.347854  6.947292
## [404,] 10.936724 13.358422
## [405,]  7.964361 16.330785
## [406,] 17.347854  6.947292
## [407,]  7.721178 16.573968
## [408,] 16.463294  7.831852
## [409,] 17.398226  6.896920
## [410,] 15.837475  8.457671
## [411,] 17.398226  6.896920
## [412,] 16.480654  7.814491
## [413,] 16.463294  7.831852
## [414,] 10.032923 14.262223
## [415,]  6.809731 17.485415
## [416,] 17.347854  6.947292
## [417,] 18.282786  6.012360
## [418,] 17.347854  6.947292
## [419,]  4.244283 20.050863
## [420,]  8.825140 15.470006
## [421,] 14.826512  9.468634
## [422,] 16.480654  7.814491
## [423,] 15.870487  8.424659
## [424,] 16.463294  7.831852
## [425,] 16.463294  7.831852
## [426,] 17.398226  6.896920
## [427,] 14.909407  9.385738
## [428,] 18.282786  6.012360
## [429,] 15.545722  8.749423
## [430,] 15.592146  8.702999
## [431,] 18.282786  6.012360
## [432,] 16.463294  7.831852
## [433,] 18.282786  6.012360
## [434,]  9.874337 14.420809
## [435,] 17.689979  6.605167
## [436,]  5.591758 18.703388
## [437,] 13.907574 10.387572
## [438,] 17.347854  6.947292
## [439,] 16.278006  8.017140
## [440,] 17.398226  6.896920
## [441,] 18.282786  6.012360
## [442,] 18.282786  6.012360
## [443,] 17.212938  7.082208
## [444,]  6.790631 17.504515
## [445,] 17.398226  6.896920
## [446,] 16.184953  8.110192
## [447,] 16.328378  7.966768
## [448,] 16.463294  7.831852
## [449,] 17.398226  6.896920
## [450,] 17.398226  6.896920
## [451,] 16.463294  7.831852
## [452,]  6.434151 17.860995
## [453,] 17.398226  6.896920
## [454,] 16.463294  7.831852
## [455,] 16.805419  7.489727
## [456,] 16.328378  7.966768
## [457,] 17.347854  6.947292
## [458,] 17.398226  6.896920
## [459,]  7.965647 16.329499
## [460,] 17.398226  6.896920
## [461,] 17.398226  6.896920
## [462,] 14.776140  9.519006
## [463,] 17.398226  6.896920
## [464,] 16.755047  7.540099
## [465,] 17.398226  6.896920
## [466,] 18.282786  6.012360
## [467,] 18.282786  6.012360
## [468,]  1.084011 23.211135
## [469,]  3.342388 20.952758
## [470,] 18.282786  6.012360
## [471,] 18.282786  6.012360
## [472,] 17.913709  6.381437
## [473,] 17.347854  6.947292
## [474,] 17.106464  7.188682
## [475,] 10.159685 14.135461
## [476,] 18.282786  6.012360
## [477,] 17.398226  6.896920
## [478,]  9.351910 14.943236
## [479,] 10.880414 13.414731
## 
## $prob
##              [,1]      [,2]
##   [1,] 0.67763718 0.3223628
##   [2,] 0.67936042 0.3206396
##   [3,] 0.52655757 0.4734424
##   [4,] 0.26006312 0.7399369
##   [5,] 0.71611943 0.2838806
##   [6,] 0.67763718 0.3223628
##   [7,] 0.67835174 0.3216483
##   [8,] 0.47826966 0.5217303
##   [9,] 0.67936042 0.3206396
##  [10,] 0.18755036 0.8124496
##  [11,] 0.50464277 0.4953572
##  [12,] 0.67763718 0.3223628
##  [13,] 0.67763718 0.3223628
##  [14,] 0.67763718 0.3223628
##  [15,] 0.22363074 0.7763693
##  [16,] 0.32649541 0.6735046
##  [17,] 0.71611943 0.2838806
##  [18,] 0.71611943 0.2838806
##  [19,] 0.52530429 0.4746957
##  [20,] 0.67763718 0.3223628
##  [21,] 0.71611943 0.2838806
##  [22,] 0.67835174 0.3216483
##  [23,] 0.31137959 0.6886204
##  [24,] 0.71611943 0.2838806
##  [25,] 0.71611943 0.2838806
##  [26,] 0.71611943 0.2838806
##  [27,] 0.63986949 0.3601305
##  [28,] 0.48640626 0.5135937
##  [29,] 0.40590804 0.5940920
##  [30,] 0.28636512 0.7136349
##  [31,] 0.49649780 0.5035022
##  [32,] 0.32188155 0.6781184
##  [33,] 0.63360171 0.3663983
##  [34,] 0.49305611 0.5069439
##  [35,] 0.37053087 0.6294691
##  [36,] 0.34043408 0.6595659
##  [37,] 0.32483344 0.6751666
##  [38,] 0.32649541 0.6735046
##  [39,] 0.45593485 0.5440652
##  [40,] 0.40590804 0.5940920
##  [41,] 0.43811678 0.5618832
##  [42,] 0.67936042 0.3206396
##  [43,] 0.34865982 0.6513402
##  [44,] 0.48215225 0.5178478
##  [45,] 0.41486883 0.5851312
##  [46,] 0.67763718 0.3223628
##  [47,] 0.32651947 0.6734805
##  [48,] 0.38844348 0.6115565
##  [49,] 0.65138762 0.3486124
##  [50,] 0.21900060 0.7809994
##  [51,] 0.69171921 0.3082808
##  [52,] 0.57178419 0.4282158
##  [53,] 0.67936042 0.3206396
##  [54,] 0.67835174 0.3216483
##  [55,] 0.67763718 0.3223628
##  [56,] 0.67763718 0.3223628
##  [57,] 0.67936042 0.3206396
##  [58,] 0.33586817 0.6641318
##  [59,] 0.47480246 0.5251975
##  [60,] 0.67763718 0.3223628
##  [61,] 0.71611943 0.2838806
##  [62,] 0.71611943 0.2838806
##  [63,] 0.75252833 0.2474717
##  [64,] 0.71611943 0.2838806
##  [65,] 0.75252833 0.2474717
##  [66,] 0.67763718 0.3223628
##  [67,] 0.18514560 0.8148544
##  [68,] 0.36368016 0.6363198
##  [69,] 0.29608535 0.7039147
##  [70,] 0.46376964 0.5362304
##  [71,] 0.35304453 0.6469555
##  [72,] 0.73733696 0.2626630
##  [73,] 0.31369331 0.6863067
##  [74,] 0.58296393 0.4170361
##  [75,] 0.27164580 0.7283542
##  [76,] 0.57350743 0.4264926
##  [77,] 0.69710384 0.3028962
##  [78,] 0.55622309 0.4437769
##  [79,] 0.67936042 0.3206396
##  [80,] 0.64087817 0.3591218
##  [81,] 0.18164819 0.8183518
##  [82,] 0.71611943 0.2838806
##  [83,] 0.19931138 0.8006886
##  [84,] 0.30582036 0.6941796
##  [85,] 0.74567762 0.2543224
##  [86,] 0.65323696 0.3467630
##  [87,] 0.14248462 0.8575154
##  [88,] 0.78164083 0.2183592
##  [89,] 0.66209569 0.3379043
##  [90,] 0.67763718 0.3223628
##  [91,] 0.75252833 0.2474717
##  [92,] 0.75252833 0.2474717
##  [93,] 0.75252833 0.2474717
##  [94,] 0.32776766 0.6722323
##  [95,] 0.69710384 0.3028962
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##  [97,] 0.50715776 0.4928422
##  [98,] 0.28691934 0.7130807
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## [105,] 0.71611943 0.2838806
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## [107,] 0.27861189 0.7213881
## [108,] 0.74315857 0.2568414
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## [110,] 0.62958365 0.3704164
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## [148,] 0.71611943 0.2838806
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## [150,] 0.41639695 0.5836031
## [151,] 0.19637882 0.8036212
## [152,] 0.71611943 0.2838806
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## [155,] 0.13634047 0.8636595
## [156,] 0.39952804 0.6004720
## [157,] 0.50464277 0.4953572
## [158,] 0.58179374 0.4182063
## [159,] 0.25792217 0.7420778
## [160,] 0.29674291 0.7032571
## [161,] 0.27834349 0.7216565
## [162,] 0.59366001 0.4063400
## [163,] 0.64893391 0.3510661
## [164,] 0.64087817 0.3591218
## [165,] 0.25060662 0.7493934
## [166,] 0.36313716 0.6368628
## [167,] 0.64916447 0.3508355
## [168,] 0.75252833 0.2474717
## [169,] 0.28636512 0.7136349
## [170,] 0.34865982 0.6513402
## [171,] 0.71611943 0.2838806
## [172,] 0.71611943 0.2838806
## [173,] 0.48035567 0.5196443
## [174,] 0.11451772 0.8854823
## [175,] 0.34218803 0.6578120
## [176,] 0.16293570 0.8370643
## [177,] 0.35069924 0.6493008
## [178,] 0.71875835 0.2812417
## [179,] 0.40032514 0.5996749
## [180,] 0.37342266 0.6265773
## [181,] 0.71611943 0.2838806
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## [183,] 0.78164083 0.2183592
## [184,] 0.71611943 0.2838806
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## [203,] 0.04952872 0.9504713
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## [211,] 0.42641973 0.5735803
## [212,] 0.67835174 0.3216483
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## [217,] 0.30518181 0.6948182
## [218,] 0.71611943 0.2838806
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## [220,] 0.71611943 0.2838806
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## [223,] 0.37121359 0.6287864
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## [225,] 0.67763718 0.3223628
## [226,] 0.74315857 0.2568414
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## [234,] 0.71611943 0.2838806
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## [239,] 0.50202032 0.4979797
## [240,] 0.67001061 0.3299894
## [241,] 0.47659904 0.5234010
## [242,] 0.22924419 0.7707558
## [243,] 0.71611943 0.2838806
## [244,] 0.48342451 0.5165755
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## [246,] 0.30655013 0.6934499
## [247,] 0.13072359 0.8692764
## [248,] 0.20452926 0.7954707
## [249,] 0.67936042 0.3206396
## [250,] 0.51062496 0.4893750
## [251,] 0.71611943 0.2838806
## [252,] 0.71611943 0.2838806
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## [266,] 0.66377876 0.3362212
## [267,] 0.29788416 0.7021158
## [268,] 0.54738397 0.4526160
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## [271,] 0.71611943 0.2838806
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## [273,] 0.71611943 0.2838806
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## [284,] 0.71611943 0.2838806
## [285,] 0.71611943 0.2838806
## [286,] 0.26400647 0.7359935
## [287,] 0.52947197 0.4705280
## [288,] 0.35655485 0.6434452
## [289,] 0.71611943 0.2838806
## [290,] 0.64667535 0.3533247
## [291,] 0.63532496 0.3646750
## [292,] 0.56860150 0.4313985
## [293,] 0.65323696 0.3467630
## [294,] 0.08618567 0.9138143
## [295,] 0.71611943 0.2838806
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## [297,] 0.67578784 0.3242122
## [298,] 0.69912311 0.3008769
## [299,] 0.74315857 0.2568414
## [300,] 0.70849287 0.2915071
## [301,] 0.49016144 0.5098386
## [302,] 0.25712597 0.7428740
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## [304,] 0.71404608 0.2859539
## [305,] 0.71404608 0.2859539
## [306,] 0.58366282 0.4163372
## [307,] 0.67173386 0.3282661
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## [309,] 0.63360171 0.3663983
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## [312,] 0.71404608 0.2859539
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## [340,] 0.75252833 0.2474717
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## [352,] 0.33230522 0.6676948
## [353,] 0.66618054 0.3338195
## [354,] 0.71404608 0.2859539
## [355,] 0.75252833 0.2474717
## [356,] 0.67763718 0.3223628
## [357,] 0.67835174 0.3216483
## [358,] 0.30604640 0.6939536
## [359,] 0.75252833 0.2474717
## [360,] 0.25718586 0.7428141
## [361,] 0.75252833 0.2474717
## [362,] 0.71404608 0.2859539
## [363,] 0.67763718 0.3223628
## [364,] 0.68964586 0.3103541
## [365,] 0.63986949 0.3601305
## [366,] 0.71611943 0.2838806
## [367,] 0.69171921 0.3082808
## [368,] 0.67763718 0.3223628
## [369,] 0.67763718 0.3223628
## [370,] 0.67763718 0.3223628
## [371,] 0.67763718 0.3223628
## [372,] 0.64087817 0.3591218
## [373,] 0.75252833 0.2474717
## [374,] 0.60819309 0.3918069
## [375,] 0.67763718 0.3223628
## [376,] 0.69171921 0.3082808
## [377,] 0.71476064 0.2852394
## [378,] 0.71476064 0.2852394
## [379,] 0.71611943 0.2838806
## [380,] 0.71611943 0.2838806
## [381,] 0.78164083 0.2183592
## [382,] 0.61608806 0.3839119
## [383,] 0.71611943 0.2838806
## [384,] 0.67763718 0.3223628
## [385,] 0.67763718 0.3223628
## [386,] 0.71611943 0.2838806
## [387,] 0.22741889 0.7725811
## [388,] 0.34191963 0.6580804
## [389,] 0.27284320 0.7271568
## [390,] 0.30336721 0.6966328
## [391,] 0.25765043 0.7423496
## [392,] 0.71611943 0.2838806
## [393,] 0.43915499 0.5608450
## [394,] 0.67763718 0.3223628
## [395,] 0.71611943 0.2838806
## [396,] 0.71611943 0.2838806
## [397,] 0.31780742 0.6821926
## [398,] 0.23614708 0.7638529
## [399,] 0.75252833 0.2474717
## [400,] 0.67001061 0.3299894
## [401,] 0.75252833 0.2474717
## [402,] 0.32347880 0.6765212
## [403,] 0.71404608 0.2859539
## [404,] 0.45016087 0.5498391
## [405,] 0.32781697 0.6721830
## [406,] 0.71404608 0.2859539
## [407,] 0.31780742 0.6821926
## [408,] 0.67763718 0.3223628
## [409,] 0.71611943 0.2838806
## [410,] 0.65187816 0.3481218
## [411,] 0.71611943 0.2838806
## [412,] 0.67835174 0.3216483
## [413,] 0.67763718 0.3223628
## [414,] 0.41295997 0.5870400
## [415,] 0.28029182 0.7197082
## [416,] 0.71404608 0.2859539
## [417,] 0.75252833 0.2474717
## [418,] 0.71404608 0.2859539
## [419,] 0.17469673 0.8253033
## [420,] 0.36324705 0.6367529
## [421,] 0.61026645 0.3897336
## [422,] 0.67835174 0.3216483
## [423,] 0.65323696 0.3467630
## [424,] 0.67763718 0.3223628
## [425,] 0.67763718 0.3223628
## [426,] 0.71611943 0.2838806
## [427,] 0.61367845 0.3863215
## [428,] 0.75252833 0.2474717
## [429,] 0.63986949 0.3601305
## [430,] 0.64178032 0.3582197
## [431,] 0.75252833 0.2474717
## [432,] 0.67763718 0.3223628
## [433,] 0.75252833 0.2474717
## [434,] 0.40643249 0.5935675
## [435,] 0.72812811 0.2718719
## [436,] 0.23015948 0.7698405
## [437,] 0.57244248 0.4275575
## [438,] 0.71404608 0.2859539
## [439,] 0.67001061 0.3299894
## [440,] 0.71611943 0.2838806
## [441,] 0.75252833 0.2474717
## [442,] 0.75252833 0.2474717
## [443,] 0.70849287 0.2915071
## [444,] 0.27950566 0.7204943
## [445,] 0.71611943 0.2838806
## [446,] 0.66618054 0.3338195
## [447,] 0.67208397 0.3279160
## [448,] 0.67763718 0.3223628
## [449,] 0.71611943 0.2838806
## [450,] 0.71611943 0.2838806
## [451,] 0.67763718 0.3223628
## [452,] 0.26483278 0.7351672
## [453,] 0.71611943 0.2838806
## [454,] 0.67763718 0.3223628
## [455,] 0.69171921 0.3082808
## [456,] 0.67208397 0.3279160
## [457,] 0.71404608 0.2859539
## [458,] 0.71611943 0.2838806
## [459,] 0.32786989 0.6721301
## [460,] 0.71611943 0.2838806
## [461,] 0.71611943 0.2838806
## [462,] 0.60819309 0.3918069
## [463,] 0.71611943 0.2838806
## [464,] 0.68964586 0.3103541
## [465,] 0.71611943 0.2838806
## [466,] 0.75252833 0.2474717
## [467,] 0.75252833 0.2474717
## [468,] 0.04461840 0.9553816
## [469,] 0.13757430 0.8624257
## [470,] 0.75252833 0.2474717
## [471,] 0.75252833 0.2474717
## [472,] 0.73733696 0.2626630
## [473,] 0.71404608 0.2859539
## [474,] 0.70411035 0.2958896
## [475,] 0.41817756 0.5818224
## [476,] 0.75252833 0.2474717
## [477,] 0.71611943 0.2838806
## [478,] 0.38492915 0.6150709
## [479,] 0.44784314 0.5521569
## 
## $class
##   [1] "benign"    "benign"    "benign"    "malignant" "benign"    "benign"   
##   [7] "benign"    "malignant" "benign"    "malignant" "benign"    "benign"   
##  [13] "benign"    "benign"    "malignant" "malignant" "benign"    "benign"   
##  [19] "benign"    "benign"    "benign"    "benign"    "malignant" "benign"   
##  [25] "benign"    "benign"    "benign"    "malignant" "malignant" "malignant"
##  [31] "malignant" "malignant" "benign"    "malignant" "malignant" "malignant"
##  [37] "malignant" "malignant" "malignant" "malignant" "malignant" "benign"   
##  [43] "malignant" "malignant" "malignant" "benign"    "malignant" "malignant"
##  [49] "benign"    "malignant" "benign"    "benign"    "benign"    "benign"   
##  [55] "benign"    "benign"    "benign"    "malignant" "malignant" "benign"   
##  [61] "benign"    "benign"    "benign"    "benign"    "benign"    "benign"   
##  [67] "malignant" "malignant" "malignant" "malignant" "malignant" "benign"   
##  [73] "malignant" "benign"    "malignant" "benign"    "benign"    "benign"   
##  [79] "benign"    "benign"    "malignant" "benign"    "malignant" "malignant"
##  [85] "benign"    "benign"    "malignant" "benign"    "benign"    "benign"   
##  [91] "benign"    "benign"    "benign"    "malignant" "benign"    "benign"   
##  [97] "benign"    "malignant" "benign"    "malignant" "benign"    "benign"   
## [103] "malignant" "benign"    "benign"    "malignant" "malignant" "benign"   
## [109] "benign"    "benign"    "benign"    "malignant" "benign"    "benign"   
## [115] "benign"    "benign"    "benign"    "malignant" "benign"    "malignant"
## [121] "benign"    "benign"    "benign"    "malignant" "malignant" "malignant"
## [127] "benign"    "benign"    "benign"    "benign"    "malignant" "benign"   
## [133] "benign"    "benign"    "malignant" "benign"    "benign"    "malignant"
## [139] "malignant" "benign"    "malignant" "benign"    "malignant" "malignant"
## [145] "malignant" "benign"    "malignant" "benign"    "benign"    "malignant"
## [151] "malignant" "benign"    "malignant" "malignant" "malignant" "malignant"
## [157] "benign"    "benign"    "malignant" "malignant" "malignant" "benign"   
## [163] "benign"    "benign"    "malignant" "malignant" "benign"    "benign"   
## [169] "malignant" "malignant" "benign"    "benign"    "malignant" "malignant"
## [175] "malignant" "malignant" "malignant" "benign"    "malignant" "malignant"
## [181] "benign"    "malignant" "benign"    "benign"    "benign"    "benign"   
## [187] "malignant" "benign"    "malignant" "malignant" "malignant" "malignant"
## [193] "malignant" "malignant" "benign"    "malignant" "malignant" "benign"   
## [199] "benign"    "malignant" "malignant" "benign"    "malignant" "benign"   
## [205] "malignant" "malignant" "benign"    "benign"    "benign"    "benign"   
## [211] "malignant" "benign"    "malignant" "malignant" "benign"    "malignant"
## [217] "malignant" "benign"    "malignant" "benign"    "benign"    "malignant"
## [223] "malignant" "malignant" "benign"    "benign"    "malignant" "malignant"
## [229] "benign"    "benign"    "benign"    "malignant" "malignant" "benign"   
## [235] "benign"    "malignant" "benign"    "benign"    "benign"    "benign"   
## [241] "malignant" "malignant" "benign"    "malignant" "malignant" "malignant"
## [247] "malignant" "malignant" "benign"    "benign"    "benign"    "benign"   
## [253] "malignant" "malignant" "benign"    "benign"    "benign"    "benign"   
## [259] "benign"    "benign"    "benign"    "benign"    "benign"    "malignant"
## [265] "benign"    "benign"    "malignant" "benign"    "benign"    "benign"   
## [271] "benign"    "malignant" "benign"    "benign"    "benign"    "benign"   
## [277] "benign"    "malignant" "benign"    "benign"    "benign"    "benign"   
## [283] "benign"    "benign"    "benign"    "malignant" "benign"    "malignant"
## [289] "benign"    "benign"    "benign"    "benign"    "benign"    "malignant"
## [295] "benign"    "benign"    "benign"    "benign"    "benign"    "benign"   
## [301] "malignant" "malignant" "malignant" "benign"    "benign"    "benign"   
## [307] "benign"    "benign"    "benign"    "benign"    "benign"    "benign"   
## [313] "benign"    "malignant" "benign"    "benign"    "malignant" "benign"   
## [319] "benign"    "benign"    "benign"    "malignant" "benign"    "benign"   
## [325] "benign"    "benign"    "benign"    "benign"    "benign"    "benign"   
## [331] "malignant" "benign"    "malignant" "malignant" "benign"    "benign"   
## [337] "malignant" "malignant" "malignant" "benign"    "malignant" "benign"   
## [343] "malignant" "benign"    "benign"    "benign"    "benign"    "benign"   
## [349] "benign"    "benign"    "benign"    "malignant" "benign"    "benign"   
## [355] "benign"    "benign"    "benign"    "malignant" "benign"    "malignant"
## [361] "benign"    "benign"    "benign"    "benign"    "benign"    "benign"   
## [367] "benign"    "benign"    "benign"    "benign"    "benign"    "benign"   
## [373] "benign"    "benign"    "benign"    "benign"    "benign"    "benign"   
## [379] "benign"    "benign"    "benign"    "benign"    "benign"    "benign"   
## [385] "benign"    "benign"    "malignant" "malignant" "malignant" "malignant"
## [391] "malignant" "benign"    "malignant" "benign"    "benign"    "benign"   
## [397] "malignant" "malignant" "benign"    "benign"    "benign"    "malignant"
## [403] "benign"    "malignant" "malignant" "benign"    "malignant" "benign"   
## [409] "benign"    "benign"    "benign"    "benign"    "benign"    "malignant"
## [415] "malignant" "benign"    "benign"    "benign"    "malignant" "malignant"
## [421] "benign"    "benign"    "benign"    "benign"    "benign"    "benign"   
## [427] "benign"    "benign"    "benign"    "benign"    "benign"    "benign"   
## [433] "benign"    "malignant" "benign"    "malignant" "benign"    "benign"   
## [439] "benign"    "benign"    "benign"    "benign"    "benign"    "malignant"
## [445] "benign"    "benign"    "benign"    "benign"    "benign"    "benign"   
## [451] "benign"    "malignant" "benign"    "benign"    "benign"    "benign"   
## [457] "benign"    "benign"    "malignant" "benign"    "benign"    "benign"   
## [463] "benign"    "benign"    "benign"    "benign"    "benign"    "malignant"
## [469] "malignant" "benign"    "benign"    "benign"    "benign"    "benign"   
## [475] "malignant" "benign"    "benign"    "malignant" "malignant"
## 
## $importance
##     Bare.nuclei     Bl.cromatin      Cell.shape       Cell.size    Cl.thickness 
##      30.6417743       1.7409291       0.8050084      41.8577541       8.9543959 
##    Epith.c.size   Marg.adhesion         Mitoses Normal.nucleoli 
##       1.3692136       7.3254615       2.5675302       4.7379328 
## 
## $terms
## Class ~ Cl.thickness + Cell.size + Cell.shape + Marg.adhesion + 
##     Epith.c.size + Bare.nuclei + Bl.cromatin + Normal.nucleoli + 
##     Mitoses
## attr(,"variables")
## list(Class, Cl.thickness, Cell.size, Cell.shape, Marg.adhesion, 
##     Epith.c.size, Bare.nuclei, Bl.cromatin, Normal.nucleoli, 
##     Mitoses)
## attr(,"factors")
##                 Cl.thickness Cell.size Cell.shape Marg.adhesion Epith.c.size
## Class                      0         0          0             0            0
## Cl.thickness               1         0          0             0            0
## Cell.size                  0         1          0             0            0
## Cell.shape                 0         0          1             0            0
## Marg.adhesion              0         0          0             1            0
## Epith.c.size               0         0          0             0            1
## Bare.nuclei                0         0          0             0            0
## Bl.cromatin                0         0          0             0            0
## Normal.nucleoli            0         0          0             0            0
## Mitoses                    0         0          0             0            0
##                 Bare.nuclei Bl.cromatin Normal.nucleoli Mitoses
## Class                     0           0               0       0
## Cl.thickness              0           0               0       0
## Cell.size                 0           0               0       0
## Cell.shape                0           0               0       0
## Marg.adhesion             0           0               0       0
## Epith.c.size              0           0               0       0
## Bare.nuclei               1           0               0       0
## Bl.cromatin               0           1               0       0
## Normal.nucleoli           0           0               1       0
## Mitoses                   0           0               0       1
## attr(,"term.labels")
## [1] "Cl.thickness"    "Cell.size"       "Cell.shape"      "Marg.adhesion"  
## [5] "Epith.c.size"    "Bare.nuclei"     "Bl.cromatin"     "Normal.nucleoli"
## [9] "Mitoses"        
## attr(,"order")
## [1] 1 1 1 1 1 1 1 1 1
## attr(,"intercept")
## [1] 1
## attr(,"response")
## [1] 1
## attr(,".Environment")
## <environment: R_GlobalEnv>
## attr(,"predvars")
## list(Class, Cl.thickness, Cell.size, Cell.shape, Marg.adhesion, 
##     Epith.c.size, Bare.nuclei, Bl.cromatin, Normal.nucleoli, 
##     Mitoses)
## attr(,"dataClasses")
##           Class    Cl.thickness       Cell.size      Cell.shape   Marg.adhesion 
##        "factor"       "ordered"       "ordered"       "ordered"       "ordered" 
##    Epith.c.size     Bare.nuclei     Bl.cromatin Normal.nucleoli         Mitoses 
##       "ordered"        "factor"        "factor"        "factor"        "factor" 
## 
## $call
## boosting(formula = Class ~ ., data = bc.train, boos = FALSE, 
##     mfinal = 50, coeflearn = "Freund", control = rpart.control(maxdepth = 1, 
##         minsplit = 10, cp = 0))
## 
## attr(,"vardep.summary")
##    benign malignant 
##       311       168 
## attr(,"class")
## [1] "boosting"

# Model 2: 100 learner
set.seed(234)
boost.ada100 <- boosting(Class ~ ., data = bc.train, boos = FALSE, mfinal = 100,
                         coeflearn = "Freund",
                         control = rpart.control(maxdepth = 1, minsplit = 10, cp = 0))
boost.ada100

## $formula
## Class ~ .
## 
## $trees
## $trees[[1]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.35073070 benign (0.77410861 0.22589139)  
##   2) Cell.size=1,2 291 0.01461378 benign (0.98686034 0.01313966) *
##   3) Cell.size=3,4,5,6,7,8,9,10 188 0.05636743 malignant (0.23690224 0.76309776) *
## 
## $trees[[2]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.28384010 benign (0.8642427 0.1357573)  
##   2) Bare.nuclei=1,2,3,4 336 0.04044944 benign (0.9754087 0.0245913) *
##   3) Bare.nuclei=5,6,7,8,9,10 143 0.08027098 malignant (0.4541872 0.5458128) *
## 
## $trees[[3]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.30593700 benign (0.83731267 0.16268733)  
##   2) Bare.nuclei=1,5,7,8 321 0.09480714 benign (0.93600088 0.06399912) *
##   3) Bare.nuclei=2,3,4,6,9,10 158 0.08287293 malignant (0.47103720 0.52896280) *
## 
## $trees[[4]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.3951662 benign (0.7008386 0.2991614)  
##   2) Cell.size=1,2,3,4,5,6,7,8,9 430 0.2676721 benign (0.7757099 0.2242901) *
##   3) Cell.size=10 49 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[5]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4129529 malignant (0.3310271 0.6689729)  
##   2) Marg.adhesion=1,2 317 0.1051429 benign (0.6373274 0.3626726) *
##   3) Marg.adhesion=3,4,5,6,7,8,9,10 162 0.1502891 malignant (0.1799101 0.8200899) *
## 
## $trees[[6]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4705728 malignant (0.4413488 0.5586512)  
##   2) Cl.thickness=1,2,3,4,5,6,7,8 418 0.3588866 benign (0.5382002 0.4617998) *
##   3) Cl.thickness=9,10 61 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[7]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.3669965 malignant (0.2515718 0.7484282) *
## 
## $trees[[8]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.50000000 malignant (0.5000000 0.5000000)  
##   2) Normal.nucleoli=1,2,3,5,7,8 403 0.27951890 benign (0.6146659 0.3853341) *
##   3) Normal.nucleoli=4,6,9,10 76 0.05412526 malignant (0.1971013 0.8028987) *
## 
## $trees[[9]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.41567430 malignant (0.3360129 0.6639871)  
##   2) Bare.nuclei=2,3,5,7,8 89 0.26110990 benign (0.4744262 0.5255738) *
##   3) Bare.nuclei=1,4,6,9,10 390 0.08434473 malignant (0.1565713 0.8434287) *
## 
## $trees[[10]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.37517690 malignant (0.2649998 0.7350002)  
##   2) Cl.thickness=1,2,3 207 0.05427183 benign (0.6200856 0.3799144) *
##   3) Cl.thickness=4,5,6,7,8,9,10 272 0.22765340 malignant (0.1932780 0.8067220) *
## 
## $trees[[11]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.49353060 benign (0.5129366 0.4870634)  
##   2) Normal.nucleoli=2,7,8 52 0.05445584 benign (0.7985195 0.2014805) *
##   3) Normal.nucleoli=1,3,4,5,6,9,10 427 0.29616030 malignant (0.4090510 0.5909490) *
## 
## $trees[[12]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4157276 benign (0.6638891 0.3361109)  
##   2) Cl.thickness=1,2,3,4,5,6,7,8 418 0.3235771 benign (0.7173322 0.2826678) *
##   3) Cl.thickness=9,10 61 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[13]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4318839 malignant (0.3662502 0.6337498) *
## 
## $trees[[14]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.500000000 benign (0.50000000 0.50000000)  
##   2) Marg.adhesion=1,2,3,4,5,6 412 0.385276100 benign (0.56233109 0.43766891) *
##   3) Marg.adhesion=7,8,9,10 67 0.004984869 malignant (0.04164164 0.95835836) *
## 
## $trees[[15]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.412310400 malignant (0.32985405 0.67014595)  
##   2) Mitoses=1,2 418 0.410805200 malignant (0.37204469 0.62795531) *
##   3) Mitoses=3,4,5,6,7,8,10 61 0.001505178 malignant (0.01032388 0.98967612) *
## 
## $trees[[16]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.500000000 benign (0.5000000 0.5000000)  
##   2) Mitoses=1,2 418 0.413873600 benign (0.5462153 0.4537847) *
##   3) Mitoses=3,4,5,6,7,8,10 61 0.001825297 malignant (0.0207534 0.9792466) *
## 
## $trees[[17]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4284951000 malignant (0.359856375 0.640143625)  
##   2) Bl.cromatin=1 100 0.0002938377 benign (0.992079603 0.007920397) *
##   3) Bl.cromatin=2,3,4,5,6,7,8,9,10 379 0.3794064000 malignant (0.332445620 0.667554380) *
## 
## $trees[[18]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4608184 benign (0.5778849 0.4221151)  
##   2) Bare.nuclei=1,2,3,4,5,7,8 379 0.3954907 benign (0.6146671 0.3853329) *
##   3) Bare.nuclei=6,9,10 100 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[19]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.44596630 malignant (0.39318014 0.60681986)  
##   2) Bl.cromatin=1,2,3,4,7 411 0.43954650 malignant (0.43081988 0.56918012) *
##   3) Bl.cromatin=5,6,8,9,10 68 0.00641977 malignant (0.05631451 0.94368549) *
## 
## $trees[[20]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.500000000 malignant (0.50000000 0.50000000)  
##   2) Bl.cromatin=1,2,3,4,7 411 0.421850500 benign (0.53878624 0.46121376) *
##   3) Bl.cromatin=5,6,8,9,10 68 0.007197595 malignant (0.08433323 0.91566677) *
## 
## $trees[[21]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.43995 malignant (0.3816076 0.6183924) *
## 
## $trees[[22]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.500000000 benign (0.50000000 0.50000000)  
##   2) Marg.adhesion=1,2,3,4,5,6 412 0.423221100 benign (0.53754739 0.46245261) *
##   3) Marg.adhesion=7,8,9,10 67 0.008054692 malignant (0.09494695 0.90505305) *
## 
## $trees[[23]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4418372 malignant (0.3852275 0.6147725)  
##   2) Normal.nucleoli=2,3,4,7 84 0.1547312 benign (0.5325152 0.4674848) *
##   3) Normal.nucleoli=1,5,6,8,9,10 395 0.2191779 malignant (0.3007283 0.6992717) *
## 
## $trees[[24]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.470906900 malignant (0.44201016 0.55798984)  
##   2) Mitoses=1,2 418 0.447787100 benign (0.48272211 0.51727789) *
##   3) Mitoses=3,4,5,6,7,8,10 61 0.001400099 malignant (0.01509502 0.98490498) *
## 
## $trees[[25]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4277530 malignant (0.3584612 0.6415388)  
##   2) Cell.shape=1,2 286 0.0930425 benign (0.5697126 0.4302874) *
##   3) Cell.shape=3,4,5,6,7,8,9,10 193 0.2629486 malignant (0.2908636 0.7091364) *
## 
## $trees[[26]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.497271100 malignant (0.49454229 0.50545771)  
##   2) Epith.c.size=2,3,4,5,6,7,8,9,10 448 0.409440900 benign (0.54194665 0.45805335) *
##   3) Epith.c.size=1 31 0.007523409 malignant (0.07387815 0.92612185) *
## 
## $trees[[27]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4290196 malignant (0.3608437 0.6391563)  
##   2) Cl.thickness=1,2,3,4,5,6,7,8 418 0.4290196 malignant (0.3926055 0.6073945) *
##   3) Cl.thickness=9,10 61 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[28]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 malignant (0.5000000 0.5000000)  
##   2) Cl.thickness=1,2,3,4,5,6,7,8 418 0.4367134 benign (0.5337812 0.4662188) *
##   3) Cl.thickness=9,10 61 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[29]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.443823800 malignant (0.38904813 0.61095187)  
##   2) Mitoses=1,2 418 0.442614000 malignant (0.42081376 0.57918624) *
##   3) Mitoses=3,4,5,6,7,8,10 61 0.001209749 malignant (0.01359437 0.98640563) *
## 
## $trees[[30]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.500000000 malignant (0.50000000 0.50000000)  
##   2) Mitoses=1,2 418 0.437028000 benign (0.53292265 0.46707735) *
##   3) Mitoses=3,4,5,6,7,8,10 61 0.001362871 malignant (0.02118401 0.97881599) *
## 
## $trees[[31]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4454905 malignant (0.3922615 0.6077385) *
## 
## $trees[[32]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Epith.c.size=2,3,4,5,6,7,8,9,10 448 0.4258433 benign (0.5346919 0.4653081) *
##   3) Epith.c.size=1 31 0.0106576 malignant (0.1256580 0.8743420) *
## 
## $trees[[33]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4464078 malignant (0.3940330 0.6059670) *
## 
## $trees[[34]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 malignant (0.5000000 0.5000000)  
##   2) Cl.thickness=1,2,3,4,5,6,7,8 418 0.4496101 benign (0.5265319 0.4734681) *
##   3) Cl.thickness=9,10 61 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[35]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.454223500 malignant (0.40920794 0.59079206)  
##   2) Marg.adhesion=1,2,3,4,5,6 412 0.446288200 malignant (0.44281177 0.55718823) *
##   3) Marg.adhesion=7,8,9,10 67 0.007935268 malignant (0.07767819 0.92232181) *
## 
## $trees[[36]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.500000000 benign (0.5000000 0.5000000)  
##   2) Marg.adhesion=1,2,3,4,5,6 412 0.428161900 benign (0.5343165 0.4656835) *
##   3) Marg.adhesion=7,8,9,10 67 0.008734983 malignant (0.1084107 0.8915893) *
## 
## $trees[[37]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.44620890 malignant (0.3936487 0.6063513)  
##   2) Bare.nuclei=1,2,3,5,7 354 0.34702260 benign (0.4586714 0.5413286) *
##   3) Bare.nuclei=4,6,8,9,10 125 0.08128174 malignant (0.2405478 0.7594522) *
## 
## $trees[[38]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.41405 malignant (0.3330337 0.6669663) *
## 
## $trees[[39]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.500000000 malignant (0.50000000 0.50000000)  
##   2) Mitoses=1,2 418 0.446919300 benign (0.52712270 0.47287730) *
##   3) Mitoses=3,4,5,6,7,8,10 61 0.001813078 malignant (0.03302882 0.96697118) *
## 
## $trees[[40]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4538760 malignant (0.4085303 0.5914697)  
##   2) Normal.nucleoli=2,4,7,8 66 0.1176017 benign (0.5659454 0.4340546) *
##   3) Normal.nucleoli=1,3,5,6,9,10 413 0.2693754 malignant (0.3431565 0.6568435) *
## 
## $trees[[41]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4985351 malignant (0.4970702 0.5029298)  
##   2) Cl.thickness=1,2,3,4,5,6 371 0.3201975 benign (0.5759397 0.4240603) *
##   3) Cl.thickness=7,8,9,10 108 0.0611014 malignant (0.2509978 0.7490022) *
## 
## $trees[[42]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4336324000 malignant (0.369562990 0.630437010)  
##   2) Bl.cromatin=1 100 0.0001767088 benign (0.992607889 0.007392111) *
##   3) Bl.cromatin=2,3,4,5,6,7,8,9,10 379 0.4026408000 malignant (0.352531012 0.647468988) *
## 
## $trees[[43]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4742712 benign (0.5513218 0.4486782)  
##   2) Epith.c.size=2,3,4,5,6,7,8,9,10 448 0.4053968 benign (0.5833990 0.4166010) *
##   3) Epith.c.size=1 31 0.0135865 malignant (0.1794319 0.8205681) *
## 
## $trees[[44]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4569431 malignant (0.4145201 0.5854799)  
##   2) Marg.adhesion=1,2,3,4,5 398 0.4316513 malignant (0.4528515 0.5471485) *
##   3) Marg.adhesion=6,7,8,9,10 81 0.0252918 malignant (0.1695645 0.8304355) *
## 
## $trees[[45]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 5.000000e-01 benign (0.500000000 0.500000000)  
##   2) Epith.c.size=1,2,3,4,5,6,7 439 4.584224e-01 benign (0.521658880 0.478341120) *
##   3) Epith.c.size=8,9,10 40 6.367098e-05 malignant (0.001529035 0.998470965) *
## 
## $trees[[46]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4616793 malignant (0.4238061 0.5761939)  
##   2) Bare.nuclei=1,2,3,5,7 354 0.3570172 benign (0.4820857 0.5179143) *
##   3) Bare.nuclei=4,6,8,9,10 125 0.0741931 malignant (0.2597852 0.7402148) *
## 
## $trees[[47]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4266523 malignant (0.3563950 0.6436050) *
## 
## $trees[[48]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.50000000 benign (0.5000000 0.5000000)  
##   2) Marg.adhesion=1,2,3,4,5,6 412 0.42870390 benign (0.5327921 0.4672079) *
##   3) Marg.adhesion=7,8,9,10 67 0.01111687 malignant (0.1348922 0.8651078) *
## 
## $trees[[49]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4490011 malignant (0.3990524 0.6009476) *
## 
## $trees[[50]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.500000000 malignant (0.50000000 0.50000000)  
##   2) Mitoses=1,2 418 0.453915600 benign (0.52335752 0.47664248) *
##   3) Mitoses=3,4,5,6,7,8,10 61 0.001596736 malignant (0.03348782 0.96651218) *
## 
## $trees[[51]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4594337 malignant (0.4193979 0.5806021) *
## 
## $trees[[52]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Cell.shape=1,2,3,4,5,6,7,8 430 0.4592696 benign (0.5212299 0.4787701) *
##   3) Cell.shape=9,10 49 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[53]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4623376 malignant (0.4251003 0.5748997) *
## 
## $trees[[54]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Normal.nucleoli=1,2,3,4,5,6,7,8 432 0.4646206 benign (0.5183385 0.4816615) *
##   3) Normal.nucleoli=9,10 47 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[55]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.46695850 malignant (0.4342044 0.5657956)  
##   2) Marg.adhesion=1 270 0.08097315 benign (0.6077687 0.3922313) *
##   3) Marg.adhesion=2,3,4,5,6,7,8,9,10 209 0.32373320 malignant (0.3854989 0.6145011) *
## 
## $trees[[56]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4797413 benign (0.5404509 0.4595491)  
##   2) Bl.cromatin=1,2,4,7 300 0.2583496 benign (0.6233530 0.3766470) *
##   3) Bl.cromatin=3,5,6,8,9,10 179 0.1259875 malignant (0.3816213 0.6183787) *
## 
## $trees[[57]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.48410280 malignant (0.4682378 0.5317622)  
##   2) Epith.c.size=2,3,4,5,6,7,8,9,10 448 0.44086020 benign (0.5004751 0.4995249) *
##   3) Epith.c.size=1 31 0.01339468 malignant (0.1434737 0.8565263) *
## 
## $trees[[58]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4459963 malignant (0.3932380 0.6067620)  
##   2) Cl.thickness=1,2,3,4,5,6,7,8 418 0.4459963 malignant (0.4147214 0.5852786) *
##   3) Cl.thickness=9,10 61 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[59]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Cl.thickness=1,2,3,4,5,6,7,8 418 0.4573128 benign (0.5222953 0.4777047) *
##   3) Cl.thickness=9,10 61 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[60]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4606705 malignant (0.4218248 0.5781752) *
## 
## $trees[[61]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Cell.shape=1,2,3,4,5,6,7,8 430 0.4590236 benign (0.5213636 0.4786364) *
##   3) Cell.shape=9,10 49 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[62]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4621274 malignant (0.4246868 0.5753132)  
##   2) Cell.shape=1,2,3,4,5,6,7,8 430 0.4621274 malignant (0.4426167 0.5573833) *
##   3) Cell.shape=9,10 49 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[63]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.500000 malignant (0.5000000 0.5000000)  
##   2) Cell.shape=1,2,3,4,5,6,7,8 430 0.464794 benign (0.5182453 0.4817547) *
##   3) Cell.shape=9,10 49 0.000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[64]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.46710990 malignant (0.4345032 0.5654968)  
##   2) Normal.nucleoli=2,8 40 0.03795363 benign (0.6777694 0.3222306) *
##   3) Normal.nucleoli=1,3,4,5,6,7,9,10 439 0.37603740 malignant (0.3997537 0.6002463) *
## 
## $trees[[65]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.468133000 benign (0.56347614 0.43652386)  
##   2) Mitoses=1,2 418 0.427982800 benign (0.58443285 0.41556715) *
##   3) Mitoses=3,4,5,6,7,8,10 61 0.002098782 malignant (0.05606049 0.94393951) *
## 
## $trees[[66]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4672154 malignant (0.4347115 0.5652885)  
##   2) Bare.nuclei=1,2,3,5 347 0.2872640 benign (0.5096021 0.4903979) *
##   3) Bare.nuclei=4,6,7,8,9,10 132 0.1268085 malignant (0.3117331 0.6882669) *
## 
## $trees[[67]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.44360910 malignant (0.3886347 0.6113653)  
##   2) Epith.c.size=2,3,4,5,6,7,8,9,10 448 0.42926810 malignant (0.4191256 0.5808744) *
##   3) Epith.c.size=1 31 0.01434095 malignant (0.1223047 0.8776953) *
## 
## $trees[[68]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.50000000 benign (0.5000000 0.5000000)  
##   2) Epith.c.size=2,3,4,5,6,7,8,9,10 448 0.42626240 benign (0.5316304 0.4683696) *
##   3) Epith.c.size=1 31 0.01616395 malignant (0.1797962 0.8202038) *
## 
## $trees[[69]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4521438 malignant (0.4051564 0.5948436)  
##   2) Cell.shape=1,2,3,4,5,6,7,8 430 0.4521438 malignant (0.4251315 0.5748685) *
##   3) Cell.shape=9,10 49 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[70]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Cell.shape=1,2,3,4,5,6,7,8 430 0.4605059 benign (0.5205590 0.4794410) *
##   3) Cell.shape=9,10 49 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[71]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4633971 malignant (0.4271844 0.5728156) *
## 
## $trees[[72]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 malignant (0.5000000 0.5000000)  
##   2) Cl.thickness=1,2,3,4,5,6,7,8 418 0.4623138 benign (0.5195811 0.4804189) *
##   3) Cl.thickness=9,10 61 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[73]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4649552 malignant (0.4302530 0.5697470)  
##   2) Normal.nucleoli=1,2,3,4,5,6,7,8 432 0.4649552 malignant (0.4482473 0.5517527) *
##   3) Normal.nucleoli=9,10 47 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[74]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Normal.nucleoli=1,2,3,4,5,6,7,8 432 0.4647705 benign (0.5182580 0.4817420) *
##   3) Normal.nucleoli=9,10 47 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[75]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4670894 malignant (0.4344627 0.5655373)  
##   2) Cell.shape=1,2,3,4,5,6,7,8 430 0.4670894 malignant (0.4511898 0.5488102) *
##   3) Cell.shape=9,10 49 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[76]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Cell.shape=1,2,3,4,5,6,7,8 430 0.4672228 benign (0.5169440 0.4830560) *
##   3) Cell.shape=9,10 49 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[77]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4692393 malignant (0.4387106 0.5612894) *
## 
## $trees[[78]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 5.000000e-01 benign (0.500000000 0.500000000)  
##   2) Epith.c.size=1,2,3,4,5,6,7 439 4.676960e-01 benign (0.516669250 0.483330750) *
##   3) Epith.c.size=8,9,10 40 4.389648e-05 malignant (0.001357013 0.998642987) *
## 
## $trees[[79]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4697009 malignant (0.4396235 0.5603765)  
##   2) Cl.thickness=1,2,3,4,5,6,7,8 418 0.4697009 malignant (0.4558305 0.5441695) *
##   3) Cl.thickness=9,10 61 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[80]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Cl.thickness=1,2,3,4,5,6,7,8 418 0.4682758 benign (0.5163818 0.4836182) *
##   3) Cl.thickness=9,10 61 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[81]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4701686 malignant (0.4405487 0.5594513) *
## 
## $trees[[82]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 malignant (0.5000000 0.5000000)  
##   2) Normal.nucleoli=1,2,3,4,5,6,7,8 432 0.4690302 benign (0.5159798 0.4840202) *
##   3) Normal.nucleoli=9,10 47 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[83]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.47083660 malignant (0.4418709 0.5581291)  
##   2) Marg.adhesion=1 270 0.09051489 benign (0.5983161 0.4016839) *
##   3) Marg.adhesion=2,3,4,5,6,7,8,9,10 209 0.31931100 malignant (0.3930954 0.6069046) *
## 
## $trees[[84]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.48205730 benign (0.5358392 0.4641608)  
##   2) Mitoses=1,2 418 0.43957150 benign (0.5571979 0.4428021) *
##   3) Mitoses=3,4,5,6,7,8,10 61 0.00313354 malignant (0.0734267 0.9265733) *
## 
## $trees[[85]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4654212 malignant (0.4311716 0.5688284)  
##   2) Normal.nucleoli=1,2,3,4,5,6,7,8 432 0.4654212 malignant (0.4476842 0.5523158) *
##   3) Normal.nucleoli=9,10 47 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[86]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Normal.nucleoli=1,2,3,4,5,6,7,8 432 0.4675785 benign (0.5167539 0.4832461) *
##   3) Normal.nucleoli=9,10 47 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[87]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4695528 malignant (0.4393305 0.5606695)  
##   2) Bare.nuclei=1,2,3,4,5,7,8 379 0.4695528 malignant (0.4558817 0.5441183) *
##   3) Bare.nuclei=6,9,10 100 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[88]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Bare.nuclei=1,2,3,4,5,7,8 379 0.4676227 benign (0.5167303 0.4832697) *
##   3) Bare.nuclei=6,9,10 100 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[89]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4695918 malignant (0.4394077 0.5605923) *
## 
## $trees[[90]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 malignant (0.5000000 0.5000000)  
##   2) Cl.thickness=1,2,3,4,5,6,7,8 418 0.4681896 benign (0.5164278 0.4835722) *
##   3) Cl.thickness=9,10 61 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[91]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.47009230 malignant (0.4403979 0.5596021)  
##   2) Normal.nucleoli=2,7,8 52 0.06539554 benign (0.6281499 0.3718501) *
##   3) Normal.nucleoli=1,3,4,5,6,9,10 427 0.34556620 malignant (0.3975756 0.6024244) *
## 
## $trees[[92]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4738611 benign (0.5521353 0.4478647)  
##   2) Cell.shape=1,2,3,4,5,6,7,8 430 0.4380506 benign (0.5714775 0.4285225) *
##   3) Cell.shape=9,10 49 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[93]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4681373 malignant (0.4365323 0.5634677) *
## 
## $trees[[94]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.500000 benign (0.5000000 0.5000000)  
##   2) Cl.thickness=1,2,3,4,5,6,7,8 418 0.468919 benign (0.5160390 0.4839610) *
##   3) Cl.thickness=9,10 61 0.000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[95]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4707379 malignant (0.4416757 0.5583243)  
##   2) Cell.shape=1,2,3,4,5,6,7,8 430 0.4707379 malignant (0.4563860 0.5436140) *
##   3) Cell.shape=9,10 49 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[96]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Cell.shape=1,2,3,4,5,6,7,8 430 0.4711348 benign (0.5148616 0.4851384) *
##   3) Cell.shape=9,10 49 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[97]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4727103 malignant (0.4455826 0.5544174) *
## 
## $trees[[98]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Bare.nuclei=1,2,3,4,5,7,8 379 0.4710989 benign (0.5148806 0.4851194) *
##   3) Bare.nuclei=6,9,10 100 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[99]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4726782 malignant (0.4455190 0.5544810) *
## 
## $trees[[100]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.500000000 benign (0.50000000 0.50000000)  
##   2) Mitoses=1,2 418 0.461405900 benign (0.51810158 0.48189842) *
##   3) Mitoses=3,4,5,6,7,8,10 61 0.003930488 malignant (0.09242853 0.90757147) *
## 
## 
## $weights
##   [1] 2.5717138 1.9856256 1.5321449 1.0064655 1.0698480 0.5801999 0.5451234
##   [8] 0.6917486 0.6390794 0.9349320 0.6163319 0.7373809 0.2741688 0.4462154
##  [15] 0.3544226 0.3404553 0.4908206 0.4242897 0.2169821 0.2857359 0.2413651
##  [22] 0.2766479 0.5154829 0.2039552 0.5928070 0.3352479 0.2858522 0.2545115
##  [29] 0.2256576 0.2476953 0.2189081 0.2553753 0.2151954 0.2022461 0.1836203
##  [36] 0.2537656 0.2887726 0.3472479 0.2057939 0.4600370 0.4840385 0.3937390
##  [43] 0.3269484 0.1726552 0.1664390 0.2769147 0.2955228 0.2418894 0.2047074
##  [50] 0.1784223 0.1626228 0.1632832 0.1509353 0.1417547 0.3858930 0.4711798
##  [57] 0.1834937 0.2168607 0.1711655 0.1576435 0.1642741 0.1517813 0.1410573
##  [64] 0.3474907 0.2815185 0.3471548 0.2265274 0.2313205 0.1920127 0.1583061
##  [71] 0.1466740 0.1510314 0.1404095 0.1411518 0.1318331 0.1312970 0.1231983
##  [78] 0.1292198 0.1213451 0.1270675 0.1194677 0.1240381 0.3646852 0.2301910
##  [85] 0.1385364 0.1298683 0.1219398 0.1296906 0.1217831 0.1274138 0.3599910
##  [92] 0.2490773 0.1276238 0.1244847 0.1171821 0.1155893 0.1092676 0.1157334
##  [99] 0.1093963 0.1388773
## 
## $votes
##             [,1]      [,2]
##   [1,] 21.361932 11.897554
##   [2,] 21.403798 11.855687
##   [3,] 17.704267 15.555219
##   [4,]  9.110859 24.148627
##   [5,] 22.296864 10.962622
##   [6,] 21.833112 11.426374
##   [7,] 20.964478 12.295008
##   [8,] 16.238870 17.020615
##   [9,] 21.403798 11.855687
##  [10,]  7.461457 25.798028
##  [11,] 16.879609 16.379877
##  [12,] 21.833112 11.426374
##  [13,] 21.361932 11.897554
##  [14,] 21.361932 11.897554
##  [15,]  8.338035 24.921450
##  [16,] 10.966362 22.293123
##  [17,] 22.768044 10.491442
##  [18,] 22.296864 10.962622
##  [19,] 16.563249 16.696237
##  [20,] 21.833112 11.426374
##  [21,] 22.768044 10.491442
##  [22,] 21.435658 11.823828
##  [23,] 10.703282 22.556203
##  [24,] 21.546286 11.713200
##  [25,] 22.768044 10.491442
##  [26,] 22.768044 10.491442
##  [27,] 20.500726 12.758760
##  [28,] 16.089396 17.170090
##  [29,] 14.059070 19.200416
##  [30,]  9.081252 24.178234
##  [31,] 16.081326 17.178159
##  [32,]  9.478139 23.781347
##  [33,] 19.541506 13.717980
##  [34,] 16.487257 16.772229
##  [35,] 12.383365 20.876120
##  [36,] 11.763174 21.496312
##  [37,] 11.333578 21.925908
##  [38,] 11.379150 21.880335
##  [39,] 15.383063 17.876423
##  [40,] 13.308492 19.950994
##  [41,] 14.726538 18.532948
##  [42,] 21.874978 11.384508
##  [43,] 11.068957 22.190528
##  [44,] 16.075784 17.183701
##  [45,] 14.097367 19.162119
##  [46,] 21.361932 11.897554
##  [47,] 12.044044 21.215441
##  [48,] 13.984205 19.275280
##  [49,] 21.152278 12.107207
##  [50,]  9.353631 23.905855
##  [51,] 22.175237 11.084249
##  [52,] 18.510820 14.748666
##  [53,] 21.403798 11.855687
##  [54,] 21.435658 11.823828
##  [55,] 21.082533 12.176952
##  [56,] 21.361932 11.897554
##  [57,] 21.874978 11.384508
##  [58,] 11.866009 21.393476
##  [59,] 15.807480 17.452006
##  [60,] 21.361932 11.897554
##  [61,] 22.017465 11.242020
##  [62,] 22.296864 10.962622
##  [63,] 22.902025 10.357461
##  [64,] 22.296864 10.962622
##  [65,] 22.902025 10.357461
##  [66,] 21.361932 11.897554
##  [67,]  5.384133 27.875352
##  [68,] 12.367105 20.892381
##  [69,]  9.445124 23.814361
##  [70,] 15.472306 17.787180
##  [71,] 12.363520 20.895965
##  [72,] 23.283527  9.975959
##  [73,] 11.704780 21.554705
##  [74,] 18.311253 14.948232
##  [75,]  8.447312 24.812173
##  [76,] 18.832085 14.427401
##  [77,] 22.306057 10.953429
##  [78,] 19.119641 14.139845
##  [79,] 21.403798 11.855687
##  [80,] 20.468866 12.790619
##  [81,]  5.364796 27.894689
##  [82,] 22.768044 10.491442
##  [83,]  8.218373 25.041113
##  [84,] 11.456611 21.802875
##  [85,] 23.356944  9.902541
##  [86,] 21.240305 12.019181
##  [87,]  6.237359 27.022127
##  [88,] 25.067377  8.192109
##  [89,] 20.984349 12.275136
##  [90,] 21.833112 11.426374
##  [91,] 23.652603  9.606882
##  [92,] 23.652603  9.606882
##  [93,] 23.652603  9.606882
##  [94,] 11.230653 22.028833
##  [95,] 22.306057 10.953429
##  [96,] 22.768044 10.491442
##  [97,] 16.593556 16.665929
##  [98,] 10.925083 22.334403
##  [99,] 20.964478 12.295008
## [100,] 13.355887 19.903599
## [101,] 21.824606 11.434880
## [102,] 23.652603  9.606882
## [103,] 11.276659 21.982827
## [104,] 22.768044 10.491442
## [105,] 22.296864 10.962622
## [106,]  4.609944 28.649542
## [107,]  9.983226 23.276260
## [108,] 23.661265  9.598220
## [109,] 22.296864 10.962622
## [110,] 18.849665 14.409821
## [111,] 22.768200 10.491286
## [112,]  6.707011 26.552474
## [113,] 22.296864 10.962622
## [114,] 21.569639 11.689846
## [115,] 23.652603  9.606882
## [116,] 22.296864 10.962622
## [117,] 22.768044 10.491442
## [118,] 10.506884 22.752602
## [119,] 22.296864 10.962622
## [120,] 10.610770 22.648715
## [121,] 21.361932 11.897554
## [122,] 20.964478 12.295008
## [123,] 23.652603  9.606882
## [124,] 12.313171 20.946315
## [125,] 10.506884 22.752602
## [126,]  7.694714 25.564771
## [127,] 21.704057 11.555429
## [128,] 21.833112 11.426374
## [129,] 22.296864 10.962622
## [130,] 22.296864 10.962622
## [131,] 16.454276 16.805210
## [132,] 19.541506 13.717980
## [133,] 23.652603  9.606882
## [134,] 22.768044 10.491442
## [135,]  8.745240 24.514245
## [136,] 22.296864 10.962622
## [137,] 22.296864 10.962622
## [138,]  5.344500 27.914986
## [139,]  9.941549 23.317937
## [140,] 20.029546 13.229940
## [141,]  4.103546 29.155940
## [142,] 22.296864 10.962622
## [143,]  2.384960 30.874525
## [144,]  3.991766 29.267719
## [145,] 11.813585 21.445901
## [146,] 22.296864 10.962622
## [147,] 10.192717 23.066769
## [148,] 21.546286 11.713200
## [149,] 17.333753 15.925732
## [150,] 14.388509 18.870977
## [151,]  7.559737 25.699749
## [152,] 22.768044 10.491442
## [153,]  6.802821 26.456664
## [154,] 15.846538 17.412947
## [155,]  4.377813 28.881673
## [156,] 13.978676 19.280809
## [157,] 16.879609 16.379877
## [158,] 19.033401 14.226084
## [159,]  9.519430 23.740056
## [160,]  9.940089 23.319397
## [161,]  9.616714 23.642772
## [162,] 19.042296 14.217190
## [163,] 20.664582 12.594904
## [164,] 19.718288 13.541198
## [165,]  9.699782 23.559703
## [166,] 13.128529 20.130956
## [167,] 20.670183 12.589302
## [168,] 23.652603  9.606882
## [169,]  9.081252 24.178234
## [170,] 11.068957 22.190528
## [171,] 22.768044 10.491442
## [172,] 22.296864 10.962622
## [173,] 16.221794 17.037692
## [174,]  4.985956 28.273530
## [175,] 11.924766 21.334720
## [176,]  6.812865 26.446621
## [177,] 11.888542 21.370943
## [178,] 23.068458 10.191027
## [179,] 13.752618 19.506867
## [180,] 11.881504 21.377981
## [181,] 22.296864 10.962622
## [182,] 13.752618 19.506867
## [183,] 24.596197  8.663288
## [184,] 22.768044 10.491442
## [185,] 22.768044 10.491442
## [186,] 22.296864 10.962622
## [187,] 10.412318 22.847167
## [188,] 22.296864 10.962622
## [189,] 10.015588 23.243898
## [190,] 10.367292 22.892194
## [191,] 11.357952 21.901533
## [192,]  6.988291 26.271194
## [193,]  2.089301 31.170184
## [194,]  7.473850 25.785636
## [195,] 22.296864 10.962622
## [196,]  9.303781 23.955704
## [197,] 10.813091 22.446394
## [198,] 17.453461 15.806024
## [199,] 22.233633 11.025853
## [200,] 13.904662 19.354824
## [201,] 11.567693 21.691792
## [202,] 22.296864 10.962622
## [203,]  2.089301 31.170184
## [204,] 22.296864 10.962622
## [205,] 10.089585 23.169901
## [206,]  5.261648 27.997837
## [207,] 22.296864 10.962622
## [208,] 20.664582 12.594904
## [209,] 22.768044 10.491442
## [210,] 23.652603  9.606882
## [211,] 14.378769 18.880716
## [212,] 21.435658 11.823828
## [213,] 13.333169 19.926316
## [214,] 11.384781 21.874705
## [215,] 22.296864 10.962622
## [216,] 16.017101 17.242384
## [217,] 10.790510 22.468975
## [218,] 22.296864 10.962622
## [219,] 10.529320 22.730166
## [220,] 22.296864 10.962622
## [221,] 23.283527  9.975959
## [222,] 13.300101 19.959385
## [223,] 12.282780 20.976705
## [224,]  8.788298 24.471187
## [225,] 21.361932 11.897554
## [226,] 23.381867  9.877619
## [227,] 12.263321 20.996164
## [228,] 11.076391 22.183094
## [229,] 23.652603  9.606882
## [230,] 22.296864 10.962622
## [231,] 21.435658 11.823828
## [232,] 11.779283 21.480203
## [233,]  9.916976 23.342510
## [234,] 22.296864 10.962622
## [235,] 23.652603  9.606882
## [236,]  7.165798 26.093687
## [237,] 22.835700 10.423785
## [238,] 16.608882 16.650604
## [239,] 16.468741 16.790744
## [240,] 20.897245 12.362241
## [241,] 16.198282 17.061203
## [242,]  7.285898 25.973588
## [243,] 22.768044 10.491442
## [244,] 15.992920 17.266566
## [245,]  6.308847 26.950639
## [246,]  9.896835 23.362650
## [247,]  4.291926 28.967560
## [248,]  7.873962 25.385524
## [249,] 21.874978 11.384508
## [250,] 17.024947 16.234538
## [251,] 22.296864 10.962622
## [252,] 22.768044 10.491442
## [253,]  5.048841 28.210644
## [254,]  6.716934 26.542551
## [255,] 20.842851 12.416635
## [256,] 23.059796 10.199689
## [257,] 21.833112 11.426374
## [258,] 21.082533 12.176952
## [259,] 22.768044 10.491442
## [260,] 23.652603  9.606882
## [261,] 22.768044 10.491442
## [262,] 18.197411 15.062074
## [263,] 23.652603  9.606882
## [264,]  8.745240 24.514245
## [265,] 23.652603  9.606882
## [266,] 20.802736 12.456750
## [267,] 11.263800 21.995686
## [268,] 17.446833 15.812653
## [269,] 25.067377  8.192109
## [270,] 23.239380 10.020106
## [271,] 22.017465 11.242020
## [272,]  7.559737 25.699749
## [273,] 22.768044 10.491442
## [274,] 22.320217 10.939268
## [275,] 22.175237 11.084249
## [276,] 22.717671 10.541814
## [277,] 25.067377  8.192109
## [278,]  8.389445 24.870041
## [279,] 23.652603  9.606882
## [280,] 24.131510  9.127975
## [281,] 22.768044 10.491442
## [282,] 21.833112 11.426374
## [283,] 18.081506 15.177979
## [284,] 22.768044 10.491442
## [285,] 22.768044 10.491442
## [286,]  8.261713 24.997772
## [287,] 17.011658 16.247827
## [288,] 13.267475 19.992011
## [289,] 22.768044 10.491442
## [290,] 21.080889 12.178596
## [291,] 19.583372 13.676114
## [292,] 18.712894 14.546592
## [293,] 21.240305 12.019181
## [294,]  3.159295 30.100191
## [295,] 22.768044 10.491442
## [296,] 22.768044 10.491442
## [297,] 22.495663 10.763822
## [298,] 21.840839 11.418647
## [299,] 24.132445  9.127040
## [300,] 21.832177 11.427309
## [301,] 16.888110 16.371376
## [302,]  8.728925 24.530561
## [303,] 15.363552 17.895934
## [304,] 22.717671 10.541814
## [305,] 22.717671 10.541814
## [306,] 18.452258 14.807228
## [307,] 20.939111 12.320374
## [308,] 22.759538 10.499948
## [309,] 20.012685 13.246800
## [310,] 23.652603  9.606882
## [311,] 23.652603  9.606882
## [312,] 22.717671 10.541814
## [313,] 22.320217 10.939268
## [314,]  7.420959 25.838527
## [315,] 18.680300 14.579186
## [316,] 21.832177 11.427309
## [317,]  6.121116 27.138370
## [318,] 22.306057 10.953429
## [319,] 20.897245 12.362241
## [320,] 25.017005  8.242481
## [321,] 22.717671 10.541814
## [322,] 11.053074 22.206412
## [323,] 22.717671 10.541814
## [324,] 22.768044 10.491442
## [325,] 21.385285 11.874200
## [326,] 22.717671 10.541814
## [327,] 22.717671 10.541814
## [328,] 23.652603  9.606882
## [329,] 22.717671 10.541814
## [330,] 22.717671 10.541814
## [331,] 10.529320 22.730166
## [332,] 21.385285 11.874200
## [333,]  5.086166 28.173320
## [334,]  9.278323 23.981163
## [335,] 19.606726 13.652760
## [336,] 21.435658 11.823828
## [337,]  2.856606 30.402880
## [338,] 15.484104 17.775381
## [339,] 16.816411 16.443075
## [340,] 23.652603  9.606882
## [341,] 10.624954 22.634532
## [342,] 20.500726 12.758760
## [343,]  7.643055 25.616431
## [344,] 17.447621 15.811865
## [345,] 21.435658 11.823828
## [346,] 21.833112 11.426374
## [347,] 21.833112 11.426374
## [348,] 21.361932 11.897554
## [349,] 21.082533 12.176952
## [350,] 21.361932 11.897554
## [351,] 23.652603  9.606882
## [352,] 11.626654 21.632832
## [353,] 21.207616 12.051870
## [354,] 22.717671 10.541814
## [355,] 23.652603  9.606882
## [356,] 21.833112 11.426374
## [357,] 21.435658 11.823828
## [358,] 10.990923 22.268563
## [359,] 23.652603  9.606882
## [360,]  9.803849 23.455637
## [361,] 22.902025 10.357461
## [362,] 22.717671 10.541814
## [363,] 21.361932 11.897554
## [364,] 21.374286 11.885200
## [365,] 20.500726 12.758760
## [366,] 22.768044 10.491442
## [367,] 20.953479 12.306007
## [368,] 21.361932 11.897554
## [369,] 21.361932 11.897554
## [370,] 21.833112 11.426374
## [371,] 21.833112 11.426374
## [372,] 20.940046 12.319440
## [373,] 23.652603  9.606882
## [374,] 20.145957 13.113528
## [375,] 21.833112 11.426374
## [376,] 21.424658 11.834827
## [377,] 22.320217 10.939268
## [378,] 22.320217 10.939268
## [379,] 22.768044 10.491442
## [380,] 22.296864 10.962622
## [381,] 24.316799  8.942687
## [382,] 21.045248 12.214237
## [383,] 22.768044 10.491442
## [384,] 21.361932 11.897554
## [385,] 21.361932 11.897554
## [386,] 22.768044 10.491442
## [387,]  7.420959 25.838527
## [388,] 13.084226 20.175260
## [389,] 10.075662 23.183823
## [390,] 11.504093 21.755392
## [391,]  8.155438 25.104047
## [392,] 22.768044 10.491442
## [393,] 14.947402 18.312084
## [394,] 21.833112 11.426374
## [395,] 22.768044 10.491442
## [396,] 22.296864 10.962622
## [397,] 10.624954 22.634532
## [398,]  8.755896 24.503590
## [399,] 23.652603  9.606882
## [400,] 20.897245 12.362241
## [401,] 23.652603  9.606882
## [402,] 11.777041 21.482445
## [403,] 22.717671 10.541814
## [404,] 15.084783 18.174702
## [405,] 11.991022 21.268464
## [406,] 22.717671 10.541814
## [407,] 11.747838 21.511647
## [408,] 21.833112 11.426374
## [409,] 22.768044 10.491442
## [410,] 20.792478 12.467007
## [411,] 22.768044 10.491442
## [412,] 21.435658 11.823828
## [413,] 21.833112 11.426374
## [414,] 13.644169 19.615316
## [415,] 10.365211 22.894274
## [416,] 21.967093 11.292393
## [417,] 23.652603  9.606882
## [418,] 22.717671 10.541814
## [419,]  7.569780 25.689705
## [420,] 11.850315 21.409170
## [421,] 20.196330 13.063156
## [422,] 21.435658 11.823828
## [423,] 21.240305 12.019181
## [424,] 21.833112 11.426374
## [425,] 21.833112 11.426374
## [426,] 22.768044 10.491442
## [427,] 19.528647 13.730839
## [428,] 23.652603  9.606882
## [429,] 19.750148 13.509338
## [430,] 20.614809 12.644677
## [431,] 23.652603  9.606882
## [432,] 21.833112 11.426374
## [433,] 23.652603  9.606882
## [434,] 13.485583 19.773902
## [435,] 23.059796 10.199689
## [436,]  7.851255 25.408230
## [437,] 18.526813 14.732673
## [438,] 22.717671 10.541814
## [439,] 20.897245 12.362241
## [440,] 22.768044 10.491442
## [441,] 23.652603  9.606882
## [442,] 23.652603  9.606882
## [443,] 21.832177 11.427309
## [444,]  8.499186 24.760299
## [445,] 22.768044 10.491442
## [446,] 20.457038 12.802448
## [447,] 20.947617 12.311868
## [448,] 21.833112 11.426374
## [449,] 22.296864 10.962622
## [450,] 22.768044 10.491442
## [451,] 21.833112 11.426374
## [452,] 10.517706 22.741779
## [453,] 22.768044 10.491442
## [454,] 21.361932 11.897554
## [455,] 22.175237 11.084249
## [456,] 20.947617 12.311868
## [457,] 21.967093 11.292393
## [458,] 22.296864 10.962622
## [459,] 10.925189 22.334297
## [460,] 21.546286 11.713200
## [461,] 22.296864 10.962622
## [462,] 20.145957 13.113528
## [463,] 22.768044 10.491442
## [464,] 22.124864 11.134621
## [465,] 22.768044 10.491442
## [466,] 23.652603  9.606882
## [467,] 23.652603  9.606882
## [468,]  1.628045 31.631441
## [469,]  5.124397 28.135088
## [470,] 23.652603  9.606882
## [471,] 23.652603  9.606882
## [472,] 23.283527  9.975959
## [473,] 22.717671 10.541814
## [474,] 21.825694 11.433791
## [475,] 13.656040 19.603446
## [476,] 23.652603  9.606882
## [477,] 22.768044 10.491442
## [478,] 11.840272 21.419213
## [479,] 15.028474 18.231012
## 
## $prob
##              [,1]      [,2]
##   [1,] 0.64228088 0.3577191
##   [2,] 0.64353967 0.3564603
##   [3,] 0.53230730 0.4676927
##   [4,] 0.27393265 0.7260674
##   [5,] 0.67039112 0.3296089
##   [6,] 0.65644766 0.3435523
##   [7,] 0.63033079 0.3696692
##   [8,] 0.48824779 0.5117522
##   [9,] 0.64353967 0.3564603
##  [10,] 0.22434073 0.7756593
##  [11,] 0.50751263 0.4924874
##  [12,] 0.65644766 0.3435523
##  [13,] 0.64228088 0.3577191
##  [14,] 0.64228088 0.3577191
##  [15,] 0.25069646 0.7493035
##  [16,] 0.32972135 0.6702787
##  [17,] 0.68455790 0.3154421
##  [18,] 0.67039112 0.3296089
##  [19,] 0.49800076 0.5019992
##  [20,] 0.65644766 0.3435523
##  [21,] 0.68455790 0.3154421
##  [22,] 0.64449757 0.3555024
##  [23,] 0.32181142 0.6781886
##  [24,] 0.64782378 0.3521762
##  [25,] 0.68455790 0.3154421
##  [26,] 0.68455790 0.3154421
##  [27,] 0.61638734 0.3836127
##  [28,] 0.48375359 0.5162464
##  [29,] 0.42270858 0.5772914
##  [30,] 0.27304246 0.7269575
##  [31,] 0.48351097 0.5164890
##  [32,] 0.28497552 0.7150245
##  [33,] 0.58754684 0.4124532
##  [34,] 0.49571592 0.5042841
##  [35,] 0.37232582 0.6276742
##  [36,] 0.35367876 0.6463212
##  [37,] 0.34076227 0.6592377
##  [38,] 0.34213248 0.6578675
##  [39,] 0.46251655 0.5374834
##  [40,] 0.40014124 0.5998588
##  [41,] 0.44277707 0.5572229
##  [42,] 0.65770645 0.3422936
##  [43,] 0.33280603 0.6671940
##  [44,] 0.48334434 0.5166557
##  [45,] 0.42386003 0.5761400
##  [46,] 0.64228088 0.3577191
##  [47,] 0.36212359 0.6378764
##  [48,] 0.42045766 0.5795423
##  [49,] 0.63597731 0.3640227
##  [50,] 0.28123198 0.7187680
##  [51,] 0.66673420 0.3332658
##  [52,] 0.55655761 0.4434424
##  [53,] 0.64353967 0.3564603
##  [54,] 0.64449757 0.3555024
##  [55,] 0.63388032 0.3661197
##  [56,] 0.64228088 0.3577191
##  [57,] 0.65770645 0.3422936
##  [58,] 0.35677068 0.6432293
##  [59,] 0.47527733 0.5247227
##  [60,] 0.64228088 0.3577191
##  [61,] 0.66199056 0.3380094
##  [62,] 0.67039112 0.3296089
##  [63,] 0.68858626 0.3114137
##  [64,] 0.67039112 0.3296089
##  [65,] 0.68858626 0.3114137
##  [66,] 0.64228088 0.3577191
##  [67,] 0.16188264 0.8381174
##  [68,] 0.37183693 0.6281631
##  [69,] 0.28398287 0.7160171
##  [70,] 0.46519979 0.5348002
##  [71,] 0.37172915 0.6282708
##  [72,] 0.70005672 0.2999433
##  [73,] 0.35192307 0.6480769
##  [74,] 0.55055732 0.4494427
##  [75,] 0.25398205 0.7460180
##  [76,] 0.56621695 0.4337830
##  [77,] 0.67066753 0.3293325
##  [78,] 0.57486279 0.4251372
##  [79,] 0.64353967 0.3564603
##  [80,] 0.61542943 0.3845706
##  [81,] 0.16130124 0.8386988
##  [82,] 0.68455790 0.3154421
##  [83,] 0.24709861 0.7529014
##  [84,] 0.34446145 0.6555385
##  [85,] 0.70226415 0.2977359
##  [86,] 0.63862397 0.3613760
##  [87,] 0.18753623 0.8124638
##  [88,] 0.75369106 0.2463089
##  [89,] 0.63092825 0.3690717
##  [90,] 0.65644766 0.3435523
##  [91,] 0.71115361 0.2888464
##  [92,] 0.71115361 0.2888464
##  [93,] 0.71115361 0.2888464
##  [94,] 0.33766766 0.6623323
##  [95,] 0.67066753 0.3293325
##  [96,] 0.68455790 0.3154421
##  [97,] 0.49891200 0.5010880
##  [98,] 0.32848021 0.6715198
##  [99,] 0.63033079 0.3696692
## [100,] 0.40156624 0.5984338
## [101,] 0.65619192 0.3438081
## [102,] 0.71115361 0.2888464
## [103,] 0.33905090 0.6609491
## [104,] 0.68455790 0.3154421
## [105,] 0.67039112 0.3296089
## [106,] 0.13860539 0.8613946
## [107,] 0.30016176 0.6998382
## [108,] 0.71141405 0.2885860
## [109,] 0.67039112 0.3296089
## [110,] 0.56674553 0.4332545
## [111,] 0.68456260 0.3154374
## [112,] 0.20165710 0.7983429
## [113,] 0.67039112 0.3296089
## [114,] 0.64852594 0.3514741
## [115,] 0.71115361 0.2888464
## [116,] 0.67039112 0.3296089
## [117,] 0.68455790 0.3154421
## [118,] 0.31590639 0.6840936
## [119,] 0.67039112 0.3296089
## [120,] 0.31902989 0.6809701
## [121,] 0.64228088 0.3577191
## [122,] 0.63033079 0.3696692
## [123,] 0.71115361 0.2888464
## [124,] 0.37021531 0.6297847
## [125,] 0.31590639 0.6840936
## [126,] 0.23135398 0.7686460
## [127,] 0.65256742 0.3474326
## [128,] 0.65644766 0.3435523
## [129,] 0.67039112 0.3296089
## [130,] 0.67039112 0.3296089
## [131,] 0.49472430 0.5052757
## [132,] 0.58754684 0.4124532
## [133,] 0.71115361 0.2888464
## [134,] 0.68455790 0.3154421
## [135,] 0.26293973 0.7370603
## [136,] 0.67039112 0.3296089
## [137,] 0.67039112 0.3296089
## [138,] 0.16069100 0.8393090
## [139,] 0.29890868 0.7010913
## [140,] 0.60222056 0.3977794
## [141,] 0.12337972 0.8766203
## [142,] 0.67039112 0.3296089
## [143,] 0.07170767 0.9282923
## [144,] 0.12001888 0.8799811
## [145,] 0.35519445 0.6448056
## [146,] 0.67039112 0.3296089
## [147,] 0.30646044 0.6935396
## [148,] 0.64782378 0.3521762
## [149,] 0.52116721 0.4788328
## [150,] 0.43261370 0.5673863
## [151,] 0.22729567 0.7727043
## [152,] 0.68455790 0.3154421
## [153,] 0.20453778 0.7954622
## [154,] 0.47645170 0.5235483
## [155,] 0.13162599 0.8683740
## [156,] 0.42029142 0.5797086
## [157,] 0.50751263 0.4924874
## [158,] 0.57226987 0.4277301
## [159,] 0.28621698 0.7137830
## [160,] 0.29886479 0.7011352
## [161,] 0.28914199 0.7108580
## [162,] 0.57253729 0.4274627
## [163,] 0.62131393 0.3786861
## [164,] 0.59286209 0.4071379
## [165,] 0.29163958 0.7083604
## [166,] 0.39473037 0.6052696
## [167,] 0.62148235 0.3785177
## [168,] 0.71115361 0.2888464
## [169,] 0.27304246 0.7269575
## [170,] 0.33280603 0.6671940
## [171,] 0.68455790 0.3154421
## [172,] 0.67039112 0.3296089
## [173,] 0.48773436 0.5122656
## [174,] 0.14991079 0.8500892
## [175,] 0.35853730 0.6414627
## [176,] 0.20483975 0.7951603
## [177,] 0.35744817 0.6425518
## [178,] 0.69359035 0.3064096
## [179,] 0.41349462 0.5865054
## [180,] 0.35723656 0.6427634
## [181,] 0.67039112 0.3296089
## [182,] 0.41349462 0.5865054
## [183,] 0.73952429 0.2604757
## [184,] 0.68455790 0.3154421
## [185,] 0.68455790 0.3154421
## [186,] 0.67039112 0.3296089
## [187,] 0.31306312 0.6869369
## [188,] 0.67039112 0.3296089
## [189,] 0.30113478 0.6988652
## [190,] 0.31170933 0.6882907
## [191,] 0.34149513 0.6585049
## [192,] 0.21011423 0.7898858
## [193,] 0.06281821 0.9371818
## [194,] 0.22471334 0.7752867
## [195,] 0.67039112 0.3296089
## [196,] 0.27973317 0.7202668
## [197,] 0.32511301 0.6748870
## [198,] 0.52476642 0.4752336
## [199,] 0.66848997 0.3315100
## [200,] 0.41806606 0.5819339
## [201,] 0.34780133 0.6521987
## [202,] 0.67039112 0.3296089
## [203,] 0.06281821 0.9371818
## [204,] 0.67039112 0.3296089
## [205,] 0.30335961 0.6966404
## [206,] 0.15819994 0.8418001
## [207,] 0.67039112 0.3296089
## [208,] 0.62131393 0.3786861
## [209,] 0.68455790 0.3154421
## [210,] 0.71115361 0.2888464
## [211,] 0.43232086 0.5676791
## [212,] 0.64449757 0.3555024
## [213,] 0.40088321 0.5991168
## [214,] 0.34230178 0.6576982
## [215,] 0.67039112 0.3296089
## [216,] 0.48157995 0.5184201
## [217,] 0.32443407 0.6755659
## [218,] 0.67039112 0.3296089
## [219,] 0.31658097 0.6834190
## [220,] 0.67039112 0.3296089
## [221,] 0.70005672 0.2999433
## [222,] 0.39988896 0.6001110
## [223,] 0.36930157 0.6306984
## [224,] 0.26423435 0.7357657
## [225,] 0.64228088 0.3577191
## [226,] 0.70301349 0.2969865
## [227,] 0.36871650 0.6312835
## [228,] 0.33302954 0.6669705
## [229,] 0.71115361 0.2888464
## [230,] 0.67039112 0.3296089
## [231,] 0.64449757 0.3555024
## [232,] 0.35416310 0.6458369
## [233,] 0.29816986 0.7018301
## [234,] 0.67039112 0.3296089
## [235,] 0.71115361 0.2888464
## [236,] 0.21545127 0.7845487
## [237,] 0.68659211 0.3134079
## [238,] 0.49937277 0.5006272
## [239,] 0.49515923 0.5048408
## [240,] 0.62830932 0.3716907
## [241,] 0.48702745 0.5129725
## [242,] 0.21906225 0.7809378
## [243,] 0.68455790 0.3154421
## [244,] 0.48085289 0.5191471
## [245,] 0.18968565 0.8103144
## [246,] 0.29756429 0.7024357
## [247,] 0.12904366 0.8709563
## [248,] 0.23674335 0.7632566
## [249,] 0.65770645 0.3422936
## [250,] 0.51188246 0.4881175
## [251,] 0.67039112 0.3296089
## [252,] 0.68455790 0.3154421
## [253,] 0.15180155 0.8481985
## [254,] 0.20195545 0.7980446
## [255,] 0.62667388 0.3733261
## [256,] 0.69332991 0.3066701
## [257,] 0.65644766 0.3435523
## [258,] 0.63388032 0.3661197
## [259,] 0.68455790 0.3154421
## [260,] 0.71115361 0.2888464
## [261,] 0.68455790 0.3154421
## [262,] 0.54713447 0.4528655
## [263,] 0.71115361 0.2888464
## [264,] 0.26293973 0.7370603
## [265,] 0.71115361 0.2888464
## [266,] 0.62546776 0.3745322
## [267,] 0.33866428 0.6613357
## [268,] 0.52456713 0.4754329
## [269,] 0.75369106 0.2463089
## [270,] 0.69872937 0.3012706
## [271,] 0.66199056 0.3380094
## [272,] 0.22729567 0.7727043
## [273,] 0.68455790 0.3154421
## [274,] 0.67109328 0.3289067
## [275,] 0.66673420 0.3332658
## [276,] 0.68304337 0.3169566
## [277,] 0.75369106 0.2463089
## [278,] 0.25224218 0.7477578
## [279,] 0.71115361 0.2888464
## [280,] 0.72555272 0.2744473
## [281,] 0.68455790 0.3154421
## [282,] 0.65644766 0.3435523
## [283,] 0.54364961 0.4563504
## [284,] 0.68455790 0.3154421
## [285,] 0.68455790 0.3154421
## [286,] 0.24840172 0.7515983
## [287,] 0.51148290 0.4885171
## [288,] 0.39890799 0.6010920
## [289,] 0.68455790 0.3154421
## [290,] 0.63383089 0.3661691
## [291,] 0.58880562 0.4111944
## [292,] 0.56263330 0.4373667
## [293,] 0.63862397 0.3613760
## [294,] 0.09498928 0.9050107
## [295,] 0.68455790 0.3154421
## [296,] 0.68455790 0.3154421
## [297,] 0.67636835 0.3236317
## [298,] 0.65668000 0.3433200
## [299,] 0.72558083 0.2744192
## [300,] 0.65641956 0.3435804
## [301,] 0.50776822 0.4922318
## [302,] 0.26244918 0.7375508
## [303,] 0.46192993 0.5380701
## [304,] 0.68304337 0.3169566
## [305,] 0.68304337 0.3169566
## [306,] 0.55479685 0.4452032
## [307,] 0.62956811 0.3704319
## [308,] 0.68430215 0.3156978
## [309,] 0.60171362 0.3982864
## [310,] 0.71115361 0.2888464
## [311,] 0.71115361 0.2888464
## [312,] 0.68304337 0.3169566
## [313,] 0.67109328 0.3289067
## [314,] 0.22312308 0.7768769
## [315,] 0.56165329 0.4383467
## [316,] 0.65641956 0.3435804
## [317,] 0.18404121 0.8159588
## [318,] 0.67066753 0.3293325
## [319,] 0.62830932 0.3716907
## [320,] 0.75217654 0.2478235
## [321,] 0.68304337 0.3169566
## [322,] 0.33232846 0.6676715
## [323,] 0.68304337 0.3169566
## [324,] 0.68455790 0.3154421
## [325,] 0.64298305 0.3570170
## [326,] 0.68304337 0.3169566
## [327,] 0.68304337 0.3169566
## [328,] 0.71115361 0.2888464
## [329,] 0.68304337 0.3169566
## [330,] 0.68304337 0.3169566
## [331,] 0.31658097 0.6834190
## [332,] 0.64298305 0.3570170
## [333,] 0.15292377 0.8470762
## [334,] 0.27896771 0.7210323
## [335,] 0.58950778 0.4104922
## [336,] 0.64449757 0.3555024
## [337,] 0.08588846 0.9141115
## [338,] 0.46555454 0.5344455
## [339,] 0.50561247 0.4943875
## [340,] 0.71115361 0.2888464
## [341,] 0.31945635 0.6805436
## [342,] 0.61638734 0.3836127
## [343,] 0.22980075 0.7701993
## [344,] 0.52459083 0.4754092
## [345,] 0.64449757 0.3555024
## [346,] 0.65644766 0.3435523
## [347,] 0.65644766 0.3435523
## [348,] 0.64228088 0.3577191
## [349,] 0.63388032 0.3661197
## [350,] 0.64228088 0.3577191
## [351,] 0.71115361 0.2888464
## [352,] 0.34957407 0.6504259
## [353,] 0.63764113 0.3623589
## [354,] 0.68304337 0.3169566
## [355,] 0.71115361 0.2888464
## [356,] 0.65644766 0.3435523
## [357,] 0.64449757 0.3555024
## [358,] 0.33045979 0.6695402
## [359,] 0.71115361 0.2888464
## [360,] 0.29476850 0.7052315
## [361,] 0.68858626 0.3114137
## [362,] 0.68304337 0.3169566
## [363,] 0.64228088 0.3577191
## [364,] 0.64265233 0.3573477
## [365,] 0.61638734 0.3836127
## [366,] 0.68455790 0.3154421
## [367,] 0.63000008 0.3699999
## [368,] 0.64228088 0.3577191
## [369,] 0.64228088 0.3577191
## [370,] 0.65644766 0.3435523
## [371,] 0.65644766 0.3435523
## [372,] 0.62959621 0.3704038
## [373,] 0.71115361 0.2888464
## [374,] 0.60572066 0.3942793
## [375,] 0.65644766 0.3435523
## [376,] 0.64416686 0.3558331
## [377,] 0.67109328 0.3289067
## [378,] 0.67109328 0.3289067
## [379,] 0.68455790 0.3154421
## [380,] 0.67039112 0.3296089
## [381,] 0.73112372 0.2688763
## [382,] 0.63275929 0.3672407
## [383,] 0.68455790 0.3154421
## [384,] 0.64228088 0.3577191
## [385,] 0.64228088 0.3577191
## [386,] 0.68455790 0.3154421
## [387,] 0.22312308 0.7768769
## [388,] 0.39339833 0.6066017
## [389,] 0.30294101 0.6970590
## [390,] 0.34588909 0.6541109
## [391,] 0.24520639 0.7547936
## [392,] 0.68455790 0.3154421
## [393,] 0.44941772 0.5505823
## [394,] 0.65644766 0.3435523
## [395,] 0.68455790 0.3154421
## [396,] 0.67039112 0.3296089
## [397,] 0.31945635 0.6805436
## [398,] 0.26326010 0.7367399
## [399,] 0.71115361 0.2888464
## [400,] 0.62830932 0.3716907
## [401,] 0.71115361 0.2888464
## [402,] 0.35409571 0.6459043
## [403,] 0.68304337 0.3169566
## [404,] 0.45354831 0.5464517
## [405,] 0.36052938 0.6394706
## [406,] 0.68304337 0.3169566
## [407,] 0.35321768 0.6467823
## [408,] 0.65644766 0.3435523
## [409,] 0.68455790 0.3154421
## [410,] 0.62515935 0.3748406
## [411,] 0.68455790 0.3154421
## [412,] 0.64449757 0.3555024
## [413,] 0.65644766 0.3435523
## [414,] 0.41023393 0.5897661
## [415,] 0.31164677 0.6883532
## [416,] 0.66047603 0.3395240
## [417,] 0.71115361 0.2888464
## [418,] 0.68304337 0.3169566
## [419,] 0.22759764 0.7724024
## [420,] 0.35629882 0.6437012
## [421,] 0.60723518 0.3927648
## [422,] 0.64449757 0.3555024
## [423,] 0.63862397 0.3613760
## [424,] 0.65644766 0.3435523
## [425,] 0.65644766 0.3435523
## [426,] 0.68455790 0.3154421
## [427,] 0.58716022 0.4128398
## [428,] 0.71115361 0.2888464
## [429,] 0.59382000 0.4061800
## [430,] 0.61981744 0.3801826
## [431,] 0.71115361 0.2888464
## [432,] 0.65644766 0.3435523
## [433,] 0.71115361 0.2888464
## [434,] 0.40546579 0.5945342
## [435,] 0.69332991 0.3066701
## [436,] 0.23606063 0.7639394
## [437,] 0.55703847 0.4429615
## [438,] 0.68304337 0.3169566
## [439,] 0.62830932 0.3716907
## [440,] 0.68455790 0.3154421
## [441,] 0.71115361 0.2888464
## [442,] 0.71115361 0.2888464
## [443,] 0.65641956 0.3435804
## [444,] 0.25554173 0.7444583
## [445,] 0.68455790 0.3154421
## [446,] 0.61507379 0.3849262
## [447,] 0.62982385 0.3701761
## [448,] 0.65644766 0.3435523
## [449,] 0.67039112 0.3296089
## [450,] 0.68455790 0.3154421
## [451,] 0.65644766 0.3435523
## [452,] 0.31623178 0.6837682
## [453,] 0.68455790 0.3154421
## [454,] 0.64228088 0.3577191
## [455,] 0.66673420 0.3332658
## [456,] 0.62982385 0.3701761
## [457,] 0.66047603 0.3395240
## [458,] 0.67039112 0.3296089
## [459,] 0.32848340 0.6715166
## [460,] 0.64782378 0.3521762
## [461,] 0.67039112 0.3296089
## [462,] 0.60572066 0.3942793
## [463,] 0.68455790 0.3154421
## [464,] 0.66521968 0.3347803
## [465,] 0.68455790 0.3154421
## [466,] 0.71115361 0.2888464
## [467,] 0.71115361 0.2888464
## [468,] 0.04894978 0.9510502
## [469,] 0.15407326 0.8459267
## [470,] 0.71115361 0.2888464
## [471,] 0.71115361 0.2888464
## [472,] 0.70005672 0.2999433
## [473,] 0.68304337 0.3169566
## [474,] 0.65622465 0.3437753
## [475,] 0.41059083 0.5894092
## [476,] 0.71115361 0.2888464
## [477,] 0.68455790 0.3154421
## [478,] 0.35599685 0.6440031
## [479,] 0.45185528 0.5481447
## 
## $class
##   [1] "benign"    "benign"    "benign"    "malignant" "benign"    "benign"   
##   [7] "benign"    "malignant" "benign"    "malignant" "benign"    "benign"   
##  [13] "benign"    "benign"    "malignant" "malignant" "benign"    "benign"   
##  [19] "malignant" "benign"    "benign"    "benign"    "malignant" "benign"   
##  [25] "benign"    "benign"    "benign"    "malignant" "malignant" "malignant"
##  [31] "malignant" "malignant" "benign"    "malignant" "malignant" "malignant"
##  [37] "malignant" "malignant" "malignant" "malignant" "malignant" "benign"   
##  [43] "malignant" "malignant" "malignant" "benign"    "malignant" "malignant"
##  [49] "benign"    "malignant" "benign"    "benign"    "benign"    "benign"   
##  [55] "benign"    "benign"    "benign"    "malignant" "malignant" "benign"   
##  [61] "benign"    "benign"    "benign"    "benign"    "benign"    "benign"   
##  [67] "malignant" "malignant" "malignant" "malignant" "malignant" "benign"   
##  [73] "malignant" "benign"    "malignant" "benign"    "benign"    "benign"   
##  [79] "benign"    "benign"    "malignant" "benign"    "malignant" "malignant"
##  [85] "benign"    "benign"    "malignant" "benign"    "benign"    "benign"   
##  [91] "benign"    "benign"    "benign"    "malignant" "benign"    "benign"   
##  [97] "malignant" "malignant" "benign"    "malignant" "benign"    "benign"   
## [103] "malignant" "benign"    "benign"    "malignant" "malignant" "benign"   
## [109] "benign"    "benign"    "benign"    "malignant" "benign"    "benign"   
## [115] "benign"    "benign"    "benign"    "malignant" "benign"    "malignant"
## [121] "benign"    "benign"    "benign"    "malignant" "malignant" "malignant"
## [127] "benign"    "benign"    "benign"    "benign"    "malignant" "benign"   
## [133] "benign"    "benign"    "malignant" "benign"    "benign"    "malignant"
## [139] "malignant" "benign"    "malignant" "benign"    "malignant" "malignant"
## [145] "malignant" "benign"    "malignant" "benign"    "benign"    "malignant"
## [151] "malignant" "benign"    "malignant" "malignant" "malignant" "malignant"
## [157] "benign"    "benign"    "malignant" "malignant" "malignant" "benign"   
## [163] "benign"    "benign"    "malignant" "malignant" "benign"    "benign"   
## [169] "malignant" "malignant" "benign"    "benign"    "malignant" "malignant"
## [175] "malignant" "malignant" "malignant" "benign"    "malignant" "malignant"
## [181] "benign"    "malignant" "benign"    "benign"    "benign"    "benign"   
## [187] "malignant" "benign"    "malignant" "malignant" "malignant" "malignant"
## [193] "malignant" "malignant" "benign"    "malignant" "malignant" "benign"   
## [199] "benign"    "malignant" "malignant" "benign"    "malignant" "benign"   
## [205] "malignant" "malignant" "benign"    "benign"    "benign"    "benign"   
## [211] "malignant" "benign"    "malignant" "malignant" "benign"    "malignant"
## [217] "malignant" "benign"    "malignant" "benign"    "benign"    "malignant"
## [223] "malignant" "malignant" "benign"    "benign"    "malignant" "malignant"
## [229] "benign"    "benign"    "benign"    "malignant" "malignant" "benign"   
## [235] "benign"    "malignant" "benign"    "malignant" "malignant" "benign"   
## [241] "malignant" "malignant" "benign"    "malignant" "malignant" "malignant"
## [247] "malignant" "malignant" "benign"    "benign"    "benign"    "benign"   
## [253] "malignant" "malignant" "benign"    "benign"    "benign"    "benign"   
## [259] "benign"    "benign"    "benign"    "benign"    "benign"    "malignant"
## [265] "benign"    "benign"    "malignant" "benign"    "benign"    "benign"   
## [271] "benign"    "malignant" "benign"    "benign"    "benign"    "benign"   
## [277] "benign"    "malignant" "benign"    "benign"    "benign"    "benign"   
## [283] "benign"    "benign"    "benign"    "malignant" "benign"    "malignant"
## [289] "benign"    "benign"    "benign"    "benign"    "benign"    "malignant"
## [295] "benign"    "benign"    "benign"    "benign"    "benign"    "benign"   
## [301] "benign"    "malignant" "malignant" "benign"    "benign"    "benign"   
## [307] "benign"    "benign"    "benign"    "benign"    "benign"    "benign"   
## [313] "benign"    "malignant" "benign"    "benign"    "malignant" "benign"   
## [319] "benign"    "benign"    "benign"    "malignant" "benign"    "benign"   
## [325] "benign"    "benign"    "benign"    "benign"    "benign"    "benign"   
## [331] "malignant" "benign"    "malignant" "malignant" "benign"    "benign"   
## [337] "malignant" "malignant" "benign"    "benign"    "malignant" "benign"   
## [343] "malignant" "benign"    "benign"    "benign"    "benign"    "benign"   
## [349] "benign"    "benign"    "benign"    "malignant" "benign"    "benign"   
## [355] "benign"    "benign"    "benign"    "malignant" "benign"    "malignant"
## [361] "benign"    "benign"    "benign"    "benign"    "benign"    "benign"   
## [367] "benign"    "benign"    "benign"    "benign"    "benign"    "benign"   
## [373] "benign"    "benign"    "benign"    "benign"    "benign"    "benign"   
## [379] "benign"    "benign"    "benign"    "benign"    "benign"    "benign"   
## [385] "benign"    "benign"    "malignant" "malignant" "malignant" "malignant"
## [391] "malignant" "benign"    "malignant" "benign"    "benign"    "benign"   
## [397] "malignant" "malignant" "benign"    "benign"    "benign"    "malignant"
## [403] "benign"    "malignant" "malignant" "benign"    "malignant" "benign"   
## [409] "benign"    "benign"    "benign"    "benign"    "benign"    "malignant"
## [415] "malignant" "benign"    "benign"    "benign"    "malignant" "malignant"
## [421] "benign"    "benign"    "benign"    "benign"    "benign"    "benign"   
## [427] "benign"    "benign"    "benign"    "benign"    "benign"    "benign"   
## [433] "benign"    "malignant" "benign"    "malignant" "benign"    "benign"   
## [439] "benign"    "benign"    "benign"    "benign"    "benign"    "malignant"
## [445] "benign"    "benign"    "benign"    "benign"    "benign"    "benign"   
## [451] "benign"    "malignant" "benign"    "benign"    "benign"    "benign"   
## [457] "benign"    "benign"    "malignant" "benign"    "benign"    "benign"   
## [463] "benign"    "benign"    "benign"    "benign"    "benign"    "malignant"
## [469] "malignant" "benign"    "benign"    "benign"    "benign"    "benign"   
## [475] "malignant" "benign"    "benign"    "malignant" "malignant"
## 
## $importance
##     Bare.nuclei     Bl.cromatin      Cell.shape       Cell.size    Cl.thickness 
##       29.377745        2.153168        2.063869       39.432729        9.222131 
##    Epith.c.size   Marg.adhesion         Mitoses Normal.nucleoli 
##        1.906759        7.403588        2.924061        5.515949 
## 
## $terms
## Class ~ Cl.thickness + Cell.size + Cell.shape + Marg.adhesion + 
##     Epith.c.size + Bare.nuclei + Bl.cromatin + Normal.nucleoli + 
##     Mitoses
## attr(,"variables")
## list(Class, Cl.thickness, Cell.size, Cell.shape, Marg.adhesion, 
##     Epith.c.size, Bare.nuclei, Bl.cromatin, Normal.nucleoli, 
##     Mitoses)
## attr(,"factors")
##                 Cl.thickness Cell.size Cell.shape Marg.adhesion Epith.c.size
## Class                      0         0          0             0            0
## Cl.thickness               1         0          0             0            0
## Cell.size                  0         1          0             0            0
## Cell.shape                 0         0          1             0            0
## Marg.adhesion              0         0          0             1            0
## Epith.c.size               0         0          0             0            1
## Bare.nuclei                0         0          0             0            0
## Bl.cromatin                0         0          0             0            0
## Normal.nucleoli            0         0          0             0            0
## Mitoses                    0         0          0             0            0
##                 Bare.nuclei Bl.cromatin Normal.nucleoli Mitoses
## Class                     0           0               0       0
## Cl.thickness              0           0               0       0
## Cell.size                 0           0               0       0
## Cell.shape                0           0               0       0
## Marg.adhesion             0           0               0       0
## Epith.c.size              0           0               0       0
## Bare.nuclei               1           0               0       0
## Bl.cromatin               0           1               0       0
## Normal.nucleoli           0           0               1       0
## Mitoses                   0           0               0       1
## attr(,"term.labels")
## [1] "Cl.thickness"    "Cell.size"       "Cell.shape"      "Marg.adhesion"  
## [5] "Epith.c.size"    "Bare.nuclei"     "Bl.cromatin"     "Normal.nucleoli"
## [9] "Mitoses"        
## attr(,"order")
## [1] 1 1 1 1 1 1 1 1 1
## attr(,"intercept")
## [1] 1
## attr(,"response")
## [1] 1
## attr(,".Environment")
## <environment: R_GlobalEnv>
## attr(,"predvars")
## list(Class, Cl.thickness, Cell.size, Cell.shape, Marg.adhesion, 
##     Epith.c.size, Bare.nuclei, Bl.cromatin, Normal.nucleoli, 
##     Mitoses)
## attr(,"dataClasses")
##           Class    Cl.thickness       Cell.size      Cell.shape   Marg.adhesion 
##        "factor"       "ordered"       "ordered"       "ordered"       "ordered" 
##    Epith.c.size     Bare.nuclei     Bl.cromatin Normal.nucleoli         Mitoses 
##       "ordered"        "factor"        "factor"        "factor"        "factor" 
## 
## $call
## boosting(formula = Class ~ ., data = bc.train, boos = FALSE, 
##     mfinal = 100, coeflearn = "Freund", control = rpart.control(maxdepth = 1, 
##         minsplit = 10, cp = 0))
## 
## attr(,"vardep.summary")
##    benign malignant 
##       311       168 
## attr(,"class")
## [1] "boosting"

# Model 3: 200 learner
set.seed(234)
boost.ada200 <- boosting(Class ~ ., data = bc.train, boos = FALSE, mfinal = 200,
                         coeflearn = "Freund",
                         control = rpart.control(maxdepth = 1, minsplit = 10, cp = 0))
boost.ada200

## $formula
## Class ~ .
## 
## $trees
## $trees[[1]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.35073070 benign (0.77410861 0.22589139)  
##   2) Cell.size=1,2 291 0.01461378 benign (0.98686034 0.01313966) *
##   3) Cell.size=3,4,5,6,7,8,9,10 188 0.05636743 malignant (0.23690224 0.76309776) *
## 
## $trees[[2]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.28384010 benign (0.8642427 0.1357573)  
##   2) Bare.nuclei=1,2,3,4 336 0.04044944 benign (0.9754087 0.0245913) *
##   3) Bare.nuclei=5,6,7,8,9,10 143 0.08027098 malignant (0.4541872 0.5458128) *
## 
## $trees[[3]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.30593700 benign (0.83731267 0.16268733)  
##   2) Bare.nuclei=1,5,7,8 321 0.09480714 benign (0.93600088 0.06399912) *
##   3) Bare.nuclei=2,3,4,6,9,10 158 0.08287293 malignant (0.47103720 0.52896280) *
## 
## $trees[[4]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.3951662 benign (0.7008386 0.2991614)  
##   2) Cell.size=1,2,3,4,5,6,7,8,9 430 0.2676721 benign (0.7757099 0.2242901) *
##   3) Cell.size=10 49 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[5]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4129529 malignant (0.3310271 0.6689729)  
##   2) Marg.adhesion=1,2 317 0.1051429 benign (0.6373274 0.3626726) *
##   3) Marg.adhesion=3,4,5,6,7,8,9,10 162 0.1502891 malignant (0.1799101 0.8200899) *
## 
## $trees[[6]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4705728 malignant (0.4413488 0.5586512)  
##   2) Cl.thickness=1,2,3,4,5,6,7,8 418 0.3588866 benign (0.5382002 0.4617998) *
##   3) Cl.thickness=9,10 61 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[7]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.3669965 malignant (0.2515718 0.7484282) *
## 
## $trees[[8]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.50000000 malignant (0.5000000 0.5000000)  
##   2) Normal.nucleoli=1,2,3,5,7,8 403 0.27951890 benign (0.6146659 0.3853341) *
##   3) Normal.nucleoli=4,6,9,10 76 0.05412526 malignant (0.1971013 0.8028987) *
## 
## $trees[[9]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.41567430 malignant (0.3360129 0.6639871)  
##   2) Bare.nuclei=2,3,5,7,8 89 0.26110990 benign (0.4744262 0.5255738) *
##   3) Bare.nuclei=1,4,6,9,10 390 0.08434473 malignant (0.1565713 0.8434287) *
## 
## $trees[[10]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.37517690 malignant (0.2649998 0.7350002)  
##   2) Cl.thickness=1,2,3 207 0.05427183 benign (0.6200856 0.3799144) *
##   3) Cl.thickness=4,5,6,7,8,9,10 272 0.22765340 malignant (0.1932780 0.8067220) *
## 
## $trees[[11]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.49353060 benign (0.5129366 0.4870634)  
##   2) Normal.nucleoli=2,7,8 52 0.05445584 benign (0.7985195 0.2014805) *
##   3) Normal.nucleoli=1,3,4,5,6,9,10 427 0.29616030 malignant (0.4090510 0.5909490) *
## 
## $trees[[12]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4157276 benign (0.6638891 0.3361109)  
##   2) Cl.thickness=1,2,3,4,5,6,7,8 418 0.3235771 benign (0.7173322 0.2826678) *
##   3) Cl.thickness=9,10 61 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[13]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4318839 malignant (0.3662502 0.6337498) *
## 
## $trees[[14]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.500000000 benign (0.50000000 0.50000000)  
##   2) Marg.adhesion=1,2,3,4,5,6 412 0.385276100 benign (0.56233109 0.43766891) *
##   3) Marg.adhesion=7,8,9,10 67 0.004984869 malignant (0.04164164 0.95835836) *
## 
## $trees[[15]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.412310400 malignant (0.32985405 0.67014595)  
##   2) Mitoses=1,2 418 0.410805200 malignant (0.37204469 0.62795531) *
##   3) Mitoses=3,4,5,6,7,8,10 61 0.001505178 malignant (0.01032388 0.98967612) *
## 
## $trees[[16]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.500000000 benign (0.5000000 0.5000000)  
##   2) Mitoses=1,2 418 0.413873600 benign (0.5462153 0.4537847) *
##   3) Mitoses=3,4,5,6,7,8,10 61 0.001825297 malignant (0.0207534 0.9792466) *
## 
## $trees[[17]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4284951000 malignant (0.359856375 0.640143625)  
##   2) Bl.cromatin=1 100 0.0002938377 benign (0.992079603 0.007920397) *
##   3) Bl.cromatin=2,3,4,5,6,7,8,9,10 379 0.3794064000 malignant (0.332445620 0.667554380) *
## 
## $trees[[18]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4608184 benign (0.5778849 0.4221151)  
##   2) Bare.nuclei=1,2,3,4,5,7,8 379 0.3954907 benign (0.6146671 0.3853329) *
##   3) Bare.nuclei=6,9,10 100 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[19]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.44596630 malignant (0.39318014 0.60681986)  
##   2) Bl.cromatin=1,2,3,4,7 411 0.43954650 malignant (0.43081988 0.56918012) *
##   3) Bl.cromatin=5,6,8,9,10 68 0.00641977 malignant (0.05631451 0.94368549) *
## 
## $trees[[20]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.500000000 malignant (0.50000000 0.50000000)  
##   2) Bl.cromatin=1,2,3,4,7 411 0.421850500 benign (0.53878624 0.46121376) *
##   3) Bl.cromatin=5,6,8,9,10 68 0.007197595 malignant (0.08433323 0.91566677) *
## 
## $trees[[21]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.43995 malignant (0.3816076 0.6183924) *
## 
## $trees[[22]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.500000000 benign (0.50000000 0.50000000)  
##   2) Marg.adhesion=1,2,3,4,5,6 412 0.423221100 benign (0.53754739 0.46245261) *
##   3) Marg.adhesion=7,8,9,10 67 0.008054692 malignant (0.09494695 0.90505305) *
## 
## $trees[[23]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4418372 malignant (0.3852275 0.6147725)  
##   2) Normal.nucleoli=2,3,4,7 84 0.1547312 benign (0.5325152 0.4674848) *
##   3) Normal.nucleoli=1,5,6,8,9,10 395 0.2191779 malignant (0.3007283 0.6992717) *
## 
## $trees[[24]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.470906900 malignant (0.44201016 0.55798984)  
##   2) Mitoses=1,2 418 0.447787100 benign (0.48272211 0.51727789) *
##   3) Mitoses=3,4,5,6,7,8,10 61 0.001400099 malignant (0.01509502 0.98490498) *
## 
## $trees[[25]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4277530 malignant (0.3584612 0.6415388)  
##   2) Cell.shape=1,2 286 0.0930425 benign (0.5697126 0.4302874) *
##   3) Cell.shape=3,4,5,6,7,8,9,10 193 0.2629486 malignant (0.2908636 0.7091364) *
## 
## $trees[[26]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.497271100 malignant (0.49454229 0.50545771)  
##   2) Epith.c.size=2,3,4,5,6,7,8,9,10 448 0.409440900 benign (0.54194665 0.45805335) *
##   3) Epith.c.size=1 31 0.007523409 malignant (0.07387815 0.92612185) *
## 
## $trees[[27]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4290196 malignant (0.3608437 0.6391563)  
##   2) Cl.thickness=1,2,3,4,5,6,7,8 418 0.4290196 malignant (0.3926055 0.6073945) *
##   3) Cl.thickness=9,10 61 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[28]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 malignant (0.5000000 0.5000000)  
##   2) Cl.thickness=1,2,3,4,5,6,7,8 418 0.4367134 benign (0.5337812 0.4662188) *
##   3) Cl.thickness=9,10 61 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[29]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.443823800 malignant (0.38904813 0.61095187)  
##   2) Mitoses=1,2 418 0.442614000 malignant (0.42081376 0.57918624) *
##   3) Mitoses=3,4,5,6,7,8,10 61 0.001209749 malignant (0.01359437 0.98640563) *
## 
## $trees[[30]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.500000000 malignant (0.50000000 0.50000000)  
##   2) Mitoses=1,2 418 0.437028000 benign (0.53292265 0.46707735) *
##   3) Mitoses=3,4,5,6,7,8,10 61 0.001362871 malignant (0.02118401 0.97881599) *
## 
## $trees[[31]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4454905 malignant (0.3922615 0.6077385) *
## 
## $trees[[32]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Epith.c.size=2,3,4,5,6,7,8,9,10 448 0.4258433 benign (0.5346919 0.4653081) *
##   3) Epith.c.size=1 31 0.0106576 malignant (0.1256580 0.8743420) *
## 
## $trees[[33]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4464078 malignant (0.3940330 0.6059670) *
## 
## $trees[[34]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 malignant (0.5000000 0.5000000)  
##   2) Cl.thickness=1,2,3,4,5,6,7,8 418 0.4496101 benign (0.5265319 0.4734681) *
##   3) Cl.thickness=9,10 61 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[35]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.454223500 malignant (0.40920794 0.59079206)  
##   2) Marg.adhesion=1,2,3,4,5,6 412 0.446288200 malignant (0.44281177 0.55718823) *
##   3) Marg.adhesion=7,8,9,10 67 0.007935268 malignant (0.07767819 0.92232181) *
## 
## $trees[[36]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.500000000 benign (0.5000000 0.5000000)  
##   2) Marg.adhesion=1,2,3,4,5,6 412 0.428161900 benign (0.5343165 0.4656835) *
##   3) Marg.adhesion=7,8,9,10 67 0.008734983 malignant (0.1084107 0.8915893) *
## 
## $trees[[37]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.44620890 malignant (0.3936487 0.6063513)  
##   2) Bare.nuclei=1,2,3,5,7 354 0.34702260 benign (0.4586714 0.5413286) *
##   3) Bare.nuclei=4,6,8,9,10 125 0.08128174 malignant (0.2405478 0.7594522) *
## 
## $trees[[38]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.41405 malignant (0.3330337 0.6669663) *
## 
## $trees[[39]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.500000000 malignant (0.50000000 0.50000000)  
##   2) Mitoses=1,2 418 0.446919300 benign (0.52712270 0.47287730) *
##   3) Mitoses=3,4,5,6,7,8,10 61 0.001813078 malignant (0.03302882 0.96697118) *
## 
## $trees[[40]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4538760 malignant (0.4085303 0.5914697)  
##   2) Normal.nucleoli=2,4,7,8 66 0.1176017 benign (0.5659454 0.4340546) *
##   3) Normal.nucleoli=1,3,5,6,9,10 413 0.2693754 malignant (0.3431565 0.6568435) *
## 
## $trees[[41]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4985351 malignant (0.4970702 0.5029298)  
##   2) Cl.thickness=1,2,3,4,5,6 371 0.3201975 benign (0.5759397 0.4240603) *
##   3) Cl.thickness=7,8,9,10 108 0.0611014 malignant (0.2509978 0.7490022) *
## 
## $trees[[42]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4336324000 malignant (0.369562990 0.630437010)  
##   2) Bl.cromatin=1 100 0.0001767088 benign (0.992607889 0.007392111) *
##   3) Bl.cromatin=2,3,4,5,6,7,8,9,10 379 0.4026408000 malignant (0.352531012 0.647468988) *
## 
## $trees[[43]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4742712 benign (0.5513218 0.4486782)  
##   2) Epith.c.size=2,3,4,5,6,7,8,9,10 448 0.4053968 benign (0.5833990 0.4166010) *
##   3) Epith.c.size=1 31 0.0135865 malignant (0.1794319 0.8205681) *
## 
## $trees[[44]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4569431 malignant (0.4145201 0.5854799)  
##   2) Marg.adhesion=1,2,3,4,5 398 0.4316513 malignant (0.4528515 0.5471485) *
##   3) Marg.adhesion=6,7,8,9,10 81 0.0252918 malignant (0.1695645 0.8304355) *
## 
## $trees[[45]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 5.000000e-01 benign (0.500000000 0.500000000)  
##   2) Epith.c.size=1,2,3,4,5,6,7 439 4.584224e-01 benign (0.521658880 0.478341120) *
##   3) Epith.c.size=8,9,10 40 6.367098e-05 malignant (0.001529035 0.998470965) *
## 
## $trees[[46]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4616793 malignant (0.4238061 0.5761939)  
##   2) Bare.nuclei=1,2,3,5,7 354 0.3570172 benign (0.4820857 0.5179143) *
##   3) Bare.nuclei=4,6,8,9,10 125 0.0741931 malignant (0.2597852 0.7402148) *
## 
## $trees[[47]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4266523 malignant (0.3563950 0.6436050) *
## 
## $trees[[48]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.50000000 benign (0.5000000 0.5000000)  
##   2) Marg.adhesion=1,2,3,4,5,6 412 0.42870390 benign (0.5327921 0.4672079) *
##   3) Marg.adhesion=7,8,9,10 67 0.01111687 malignant (0.1348922 0.8651078) *
## 
## $trees[[49]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4490011 malignant (0.3990524 0.6009476) *
## 
## $trees[[50]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.500000000 malignant (0.50000000 0.50000000)  
##   2) Mitoses=1,2 418 0.453915600 benign (0.52335752 0.47664248) *
##   3) Mitoses=3,4,5,6,7,8,10 61 0.001596736 malignant (0.03348782 0.96651218) *
## 
## $trees[[51]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4594337 malignant (0.4193979 0.5806021) *
## 
## $trees[[52]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Cell.shape=1,2,3,4,5,6,7,8 430 0.4592696 benign (0.5212299 0.4787701) *
##   3) Cell.shape=9,10 49 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[53]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4623376 malignant (0.4251003 0.5748997) *
## 
## $trees[[54]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Normal.nucleoli=1,2,3,4,5,6,7,8 432 0.4646206 benign (0.5183385 0.4816615) *
##   3) Normal.nucleoli=9,10 47 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[55]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.46695850 malignant (0.4342044 0.5657956)  
##   2) Marg.adhesion=1 270 0.08097315 benign (0.6077687 0.3922313) *
##   3) Marg.adhesion=2,3,4,5,6,7,8,9,10 209 0.32373320 malignant (0.3854989 0.6145011) *
## 
## $trees[[56]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4797413 benign (0.5404509 0.4595491)  
##   2) Bl.cromatin=1,2,4,7 300 0.2583496 benign (0.6233530 0.3766470) *
##   3) Bl.cromatin=3,5,6,8,9,10 179 0.1259875 malignant (0.3816213 0.6183787) *
## 
## $trees[[57]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.48410280 malignant (0.4682378 0.5317622)  
##   2) Epith.c.size=2,3,4,5,6,7,8,9,10 448 0.44086020 benign (0.5004751 0.4995249) *
##   3) Epith.c.size=1 31 0.01339468 malignant (0.1434737 0.8565263) *
## 
## $trees[[58]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4459963 malignant (0.3932380 0.6067620)  
##   2) Cl.thickness=1,2,3,4,5,6,7,8 418 0.4459963 malignant (0.4147214 0.5852786) *
##   3) Cl.thickness=9,10 61 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[59]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Cl.thickness=1,2,3,4,5,6,7,8 418 0.4573128 benign (0.5222953 0.4777047) *
##   3) Cl.thickness=9,10 61 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[60]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4606705 malignant (0.4218248 0.5781752) *
## 
## $trees[[61]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Cell.shape=1,2,3,4,5,6,7,8 430 0.4590236 benign (0.5213636 0.4786364) *
##   3) Cell.shape=9,10 49 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[62]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4621274 malignant (0.4246868 0.5753132)  
##   2) Cell.shape=1,2,3,4,5,6,7,8 430 0.4621274 malignant (0.4426167 0.5573833) *
##   3) Cell.shape=9,10 49 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[63]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.500000 malignant (0.5000000 0.5000000)  
##   2) Cell.shape=1,2,3,4,5,6,7,8 430 0.464794 benign (0.5182453 0.4817547) *
##   3) Cell.shape=9,10 49 0.000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[64]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.46710990 malignant (0.4345032 0.5654968)  
##   2) Normal.nucleoli=2,8 40 0.03795363 benign (0.6777694 0.3222306) *
##   3) Normal.nucleoli=1,3,4,5,6,7,9,10 439 0.37603740 malignant (0.3997537 0.6002463) *
## 
## $trees[[65]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.468133000 benign (0.56347614 0.43652386)  
##   2) Mitoses=1,2 418 0.427982800 benign (0.58443285 0.41556715) *
##   3) Mitoses=3,4,5,6,7,8,10 61 0.002098782 malignant (0.05606049 0.94393951) *
## 
## $trees[[66]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4672154 malignant (0.4347115 0.5652885)  
##   2) Bare.nuclei=1,2,3,5 347 0.2872640 benign (0.5096021 0.4903979) *
##   3) Bare.nuclei=4,6,7,8,9,10 132 0.1268085 malignant (0.3117331 0.6882669) *
## 
## $trees[[67]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.44360910 malignant (0.3886347 0.6113653)  
##   2) Epith.c.size=2,3,4,5,6,7,8,9,10 448 0.42926810 malignant (0.4191256 0.5808744) *
##   3) Epith.c.size=1 31 0.01434095 malignant (0.1223047 0.8776953) *
## 
## $trees[[68]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.50000000 benign (0.5000000 0.5000000)  
##   2) Epith.c.size=2,3,4,5,6,7,8,9,10 448 0.42626240 benign (0.5316304 0.4683696) *
##   3) Epith.c.size=1 31 0.01616395 malignant (0.1797962 0.8202038) *
## 
## $trees[[69]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4521438 malignant (0.4051564 0.5948436)  
##   2) Cell.shape=1,2,3,4,5,6,7,8 430 0.4521438 malignant (0.4251315 0.5748685) *
##   3) Cell.shape=9,10 49 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[70]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Cell.shape=1,2,3,4,5,6,7,8 430 0.4605059 benign (0.5205590 0.4794410) *
##   3) Cell.shape=9,10 49 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[71]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4633971 malignant (0.4271844 0.5728156) *
## 
## $trees[[72]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 malignant (0.5000000 0.5000000)  
##   2) Cl.thickness=1,2,3,4,5,6,7,8 418 0.4623138 benign (0.5195811 0.4804189) *
##   3) Cl.thickness=9,10 61 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[73]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4649552 malignant (0.4302530 0.5697470)  
##   2) Normal.nucleoli=1,2,3,4,5,6,7,8 432 0.4649552 malignant (0.4482473 0.5517527) *
##   3) Normal.nucleoli=9,10 47 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[74]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Normal.nucleoli=1,2,3,4,5,6,7,8 432 0.4647705 benign (0.5182580 0.4817420) *
##   3) Normal.nucleoli=9,10 47 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[75]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4670894 malignant (0.4344627 0.5655373)  
##   2) Cell.shape=1,2,3,4,5,6,7,8 430 0.4670894 malignant (0.4511898 0.5488102) *
##   3) Cell.shape=9,10 49 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[76]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Cell.shape=1,2,3,4,5,6,7,8 430 0.4672228 benign (0.5169440 0.4830560) *
##   3) Cell.shape=9,10 49 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[77]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4692393 malignant (0.4387106 0.5612894) *
## 
## $trees[[78]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 5.000000e-01 benign (0.500000000 0.500000000)  
##   2) Epith.c.size=1,2,3,4,5,6,7 439 4.676960e-01 benign (0.516669250 0.483330750) *
##   3) Epith.c.size=8,9,10 40 4.389648e-05 malignant (0.001357013 0.998642987) *
## 
## $trees[[79]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4697009 malignant (0.4396235 0.5603765)  
##   2) Cl.thickness=1,2,3,4,5,6,7,8 418 0.4697009 malignant (0.4558305 0.5441695) *
##   3) Cl.thickness=9,10 61 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[80]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Cl.thickness=1,2,3,4,5,6,7,8 418 0.4682758 benign (0.5163818 0.4836182) *
##   3) Cl.thickness=9,10 61 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[81]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4701686 malignant (0.4405487 0.5594513) *
## 
## $trees[[82]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 malignant (0.5000000 0.5000000)  
##   2) Normal.nucleoli=1,2,3,4,5,6,7,8 432 0.4690302 benign (0.5159798 0.4840202) *
##   3) Normal.nucleoli=9,10 47 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[83]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.47083660 malignant (0.4418709 0.5581291)  
##   2) Marg.adhesion=1 270 0.09051489 benign (0.5983161 0.4016839) *
##   3) Marg.adhesion=2,3,4,5,6,7,8,9,10 209 0.31931100 malignant (0.3930954 0.6069046) *
## 
## $trees[[84]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.48205730 benign (0.5358392 0.4641608)  
##   2) Mitoses=1,2 418 0.43957150 benign (0.5571979 0.4428021) *
##   3) Mitoses=3,4,5,6,7,8,10 61 0.00313354 malignant (0.0734267 0.9265733) *
## 
## $trees[[85]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4654212 malignant (0.4311716 0.5688284)  
##   2) Normal.nucleoli=1,2,3,4,5,6,7,8 432 0.4654212 malignant (0.4476842 0.5523158) *
##   3) Normal.nucleoli=9,10 47 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[86]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Normal.nucleoli=1,2,3,4,5,6,7,8 432 0.4675785 benign (0.5167539 0.4832461) *
##   3) Normal.nucleoli=9,10 47 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[87]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4695528 malignant (0.4393305 0.5606695)  
##   2) Bare.nuclei=1,2,3,4,5,7,8 379 0.4695528 malignant (0.4558817 0.5441183) *
##   3) Bare.nuclei=6,9,10 100 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[88]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Bare.nuclei=1,2,3,4,5,7,8 379 0.4676227 benign (0.5167303 0.4832697) *
##   3) Bare.nuclei=6,9,10 100 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[89]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4695918 malignant (0.4394077 0.5605923) *
## 
## $trees[[90]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 malignant (0.5000000 0.5000000)  
##   2) Cl.thickness=1,2,3,4,5,6,7,8 418 0.4681896 benign (0.5164278 0.4835722) *
##   3) Cl.thickness=9,10 61 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[91]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.47009230 malignant (0.4403979 0.5596021)  
##   2) Normal.nucleoli=2,7,8 52 0.06539554 benign (0.6281499 0.3718501) *
##   3) Normal.nucleoli=1,3,4,5,6,9,10 427 0.34556620 malignant (0.3975756 0.6024244) *
## 
## $trees[[92]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4738611 benign (0.5521353 0.4478647)  
##   2) Cell.shape=1,2,3,4,5,6,7,8 430 0.4380506 benign (0.5714775 0.4285225) *
##   3) Cell.shape=9,10 49 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[93]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4681373 malignant (0.4365323 0.5634677) *
## 
## $trees[[94]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.500000 benign (0.5000000 0.5000000)  
##   2) Cl.thickness=1,2,3,4,5,6,7,8 418 0.468919 benign (0.5160390 0.4839610) *
##   3) Cl.thickness=9,10 61 0.000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[95]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4707379 malignant (0.4416757 0.5583243)  
##   2) Cell.shape=1,2,3,4,5,6,7,8 430 0.4707379 malignant (0.4563860 0.5436140) *
##   3) Cell.shape=9,10 49 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[96]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Cell.shape=1,2,3,4,5,6,7,8 430 0.4711348 benign (0.5148616 0.4851384) *
##   3) Cell.shape=9,10 49 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[97]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4727103 malignant (0.4455826 0.5544174) *
## 
## $trees[[98]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Bare.nuclei=1,2,3,4,5,7,8 379 0.4710989 benign (0.5148806 0.4851194) *
##   3) Bare.nuclei=6,9,10 100 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[99]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4726782 malignant (0.4455190 0.5544810) *
## 
## $trees[[100]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.500000000 benign (0.50000000 0.50000000)  
##   2) Mitoses=1,2 418 0.461405900 benign (0.51810158 0.48189842) *
##   3) Mitoses=3,4,5,6,7,8,10 61 0.003930488 malignant (0.09242853 0.90757147) *
## 
## $trees[[101]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4681313 malignant (0.4365205 0.5634795) *
## 
## $trees[[102]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Normal.nucleoli=1,2,3,4,5,6,7,8 432 0.4717815 benign (0.5145189 0.4854811) *
##   3) Normal.nucleoli=9,10 47 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[103]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4732890000 malignant (0.446730087 0.553269913)  
##   2) Bl.cromatin=1 100 0.0001840356 benign (0.991569811 0.008430189) *
##   3) Bl.cromatin=2,3,4,5,6,7,8,9,10 379 0.4491992000 malignant (0.433942966 0.566057034) *
## 
## $trees[[104]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 4.783295e-01 benign (0.543259831 0.456740169)  
##   2) Epith.c.size=1,2,3,4,5,6,7 439 4.523842e-01 benign (0.557043600 0.442956400) *
##   3) Epith.c.size=8,9,10 40 3.584781e-05 malignant (0.001504598 0.998495402) *
## 
## $trees[[105]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.47634880 malignant (0.4528032 0.5471968)  
##   2) Bare.nuclei=1,2,3,5,7,8 367 0.45611060 benign (0.4779874 0.5220126) *
##   3) Bare.nuclei=4,6,9,10 112 0.01723261 malignant (0.1883755 0.8116245) *
## 
## $trees[[106]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4540811000 malignant (0.40893026 0.59106974)  
##   2) Cl.thickness=1 99 0.0004003021 benign (0.97825287 0.02174713) *
##   3) Cl.thickness=2,3,4,5,6,7,8,9,10 380 0.4324324000 malignant (0.39735322 0.60264678) *
## 
## $trees[[107]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4813775 benign (0.5371934 0.4628066)  
##   2) Cell.shape=1,2,3,4,5,6,7,8 430 0.4556559 benign (0.5508147 0.4491853) *
##   3) Cell.shape=9,10 49 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[108]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4763737 malignant (0.4528527 0.5471473)  
##   2) Epith.c.size=2,3,4,5,6,7,8,9,10 448 0.4562903 benign (0.4778081 0.5221919) *
##   3) Epith.c.size=1 31 0.0174523 malignant (0.1908035 0.8091965) *
## 
## $trees[[109]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.454443400 malignant (0.40963691 0.59036309)  
##   2) Mitoses=1,2 418 0.451521700 malignant (0.42558096 0.57441904) *
##   3) Mitoses=3,4,5,6,7,8,10 61 0.002921701 malignant (0.06033172 0.93966828) *
## 
## $trees[[110]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.500000000 malignant (0.50000000 0.50000000)  
##   2) Mitoses=1,2 418 0.465259600 benign (0.51638479 0.48361521) *
##   3) Mitoses=3,4,5,6,7,8,10 61 0.003214594 malignant (0.08469487 0.91530513) *
## 
## $trees[[111]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.470751 malignant (0.4417016 0.5582984) *
## 
## $trees[[112]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Normal.nucleoli=1,2,3,4,5,6,7,8 432 0.4737123 benign (0.5134987 0.4865013) *
##   3) Normal.nucleoli=9,10 47 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[113]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4750254 malignant (0.4501750 0.5498250)  
##   2) Bare.nuclei=1,2,3,5 347 0.2954442 benign (0.5100261 0.4899739) *
##   3) Bare.nuclei=4,6,7,8,9,10 132 0.1351526 malignant (0.3475983 0.6524017) *
## 
## $trees[[114]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4553828 malignant (0.4114706 0.5885294) *
## 
## $trees[[115]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.50000000 benign (0.5000000 0.5000000)  
##   2) Epith.c.size=2,3,4,5,6,7,8,9,10 448 0.43347630 benign (0.5260495 0.4739505) *
##   3) Epith.c.size=1 31 0.01887385 malignant (0.2210117 0.7789883) *
## 
## $trees[[116]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4601264 malignant (0.4207567 0.5792433) *
## 
## $trees[[117]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.50000000 benign (0.5000000 0.5000000)  
##   2) Marg.adhesion=1,2,3,4,5,6 412 0.44170290 benign (0.5238081 0.4761919) *
##   3) Marg.adhesion=7,8,9,10 67 0.01412959 malignant (0.1950881 0.8049119) *
## 
## $trees[[118]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4619332 malignant (0.4243052 0.5756948)  
##   2) Cl.thickness=1,2,3,4,5,6,7,8 418 0.4619332 malignant (0.4385877 0.5614123) *
##   3) Cl.thickness=9,10 61 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[119]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.500000 benign (0.5000000 0.5000000)  
##   2) Cl.thickness=1,2,3,4,5,6,7,8 418 0.471717 benign (0.5145531 0.4854469) *
##   3) Cl.thickness=9,10 61 0.000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[120]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4732312 malignant (0.4466155 0.5533845) *
## 
## $trees[[121]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Normal.nucleoli=1,2,3,4,5,6,7,8 432 0.4727984 benign (0.5139811 0.4860189) *
##   3) Normal.nucleoli=9,10 47 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[122]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4742019 malignant (0.4485407 0.5514593) *
## 
## $trees[[123]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Cell.shape=1,2,3,4,5,6,7,8 430 0.4740866 benign (0.5133014 0.4866986) *
##   3) Cell.shape=9,10 49 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[124]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4753635 malignant (0.4508463 0.5491537) *
## 
## $trees[[125]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.500000000 benign (0.50000000 0.50000000)  
##   2) Mitoses=1,2,8 424 0.464865500 benign (0.51633172 0.48366828) *
##   3) Mitoses=3,4,5,6,7,10 55 0.003740883 malignant (0.09622752 0.90377248) *
## 
## $trees[[126]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 4.709327e-01 malignant (0.4420611239 0.5579388761)  
##   2) Epith.c.size=1,2,3,4,5,6,7 439 4.709056e-01 malignant (0.4550268964 0.5449731036) *
##   3) Epith.c.size=8,9,10 40 2.705246e-05 malignant (0.0008894451 0.9991105549) *
## 
## $trees[[127]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 5.000000e-01 benign (0.500000000 0.500000000)  
##   2) Epith.c.size=1,2,3,4,5,6,7 439 4.744372e-01 benign (0.513102335 0.486897665) *
##   3) Epith.c.size=8,9,10 40 2.872221e-05 malignant (0.001122334 0.998877666) *
## 
## $trees[[128]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4757095 malignant (0.4515334 0.5484666) *
## 
## $trees[[129]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Normal.nucleoli=1,2,3,4,5,6,7,8 432 0.4751366 benign (0.5127487 0.4872513) *
##   3) Normal.nucleoli=9,10 47 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[130]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4763144 malignant (0.4527348 0.5472652)  
##   2) Cl.thickness=1,2,3,4,5,6,7,8 418 0.4763144 malignant (0.4653456 0.5346544) *
##   3) Cl.thickness=9,10 61 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[131]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Cl.thickness=1,2,3,4,5,6,7,8 418 0.4752406 benign (0.5126940 0.4873060) *
##   3) Cl.thickness=9,10 61 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[132]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4764088000 malignant (0.452922482 0.547077518)  
##   2) Bl.cromatin=1 100 0.0001960756 benign (0.990082405 0.009917595) *
##   3) Bl.cromatin=2,3,4,5,6,7,8,9,10 379 0.4548958000 malignant (0.441592152 0.558407848) *
## 
## $trees[[133]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 4.804754e-01 benign (0.538989746 0.461010254)  
##   2) Epith.c.size=1,2,3,4,5,6,7 439 4.580792e-01 benign (0.550813127 0.449186873) *
##   3) Epith.c.size=8,9,10 40 2.781427e-05 malignant (0.001341053 0.998658947) *
## 
## $trees[[134]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4793656 malignant (0.4588014 0.5411986)  
##   2) Cell.shape=1,2,3,4,5,6,7,8 430 0.4793656 malignant (0.4706968 0.5293032) *
##   3) Cell.shape=9,10 49 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[135]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Cell.shape=1,2,3,4,5,6,7,8 430 0.4766518 benign (0.5119532 0.4880468) *
##   3) Cell.shape=9,10 49 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[136]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4776934 malignant (0.4554755 0.5445245)  
##   2) Bl.cromatin=1,2,3,7 381 0.3154826 benign (0.5081143 0.4918857) *
##   3) Bl.cromatin=4,5,6,8,9,10 98 0.1213663 malignant (0.3492494 0.6507506) *
## 
## $trees[[137]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4552801 malignant (0.4112700 0.5887300)  
##   2) Cell.shape=4,5,6,7,8,9,10 160 0.2954379 benign (0.4765298 0.5234702) *
##   3) Cell.shape=1,2,3 319 0.1335003 malignant (0.3092046 0.6907954) *
## 
## $trees[[138]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4373553 malignant (0.3766469 0.6233531) *
## 
## $trees[[139]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.50000000 benign (0.5000000 0.5000000)  
##   2) Cl.thickness=1,2,3,4,5,6 371 0.35027660 benign (0.5480219 0.4519781) *
##   3) Cl.thickness=7,8,9,10 108 0.07529076 malignant (0.3346046 0.6653954) *
## 
## $trees[[140]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.45813640 malignant (0.4168557 0.5831443)  
##   2) Cl.thickness=8,9,10 91 0.04133692 benign (0.6378581 0.3621419) *
##   3) Cl.thickness=1,2,3,4,5,6,7 388 0.37202150 malignant (0.3859055 0.6140945) *
## 
## $trees[[141]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4766048 benign (0.5466883 0.4533117)  
##   2) Cell.shape=1,2,3,4,5,6,7,8 430 0.4494252 benign (0.5611959 0.4388041) *
##   3) Cell.shape=9,10 49 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[142]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4753171 malignant (0.4507542 0.5492458)  
##   2) Normal.nucleoli=1,2,3,4,5,6,7,8 432 0.4753171 malignant (0.4649845 0.5350155) *
##   3) Normal.nucleoli=9,10 47 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[143]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 malignant (0.5000000 0.5000000)  
##   2) Normal.nucleoli=1,2,3,4,5,6,7,8 432 0.4721401 benign (0.5143291 0.4856709) *
##   3) Normal.nucleoli=9,10 47 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[144]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4736106 malignant (0.4473677 0.5526323) *
## 
## $trees[[145]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.50000000 malignant (0.5000000 0.5000000)  
##   2) Marg.adhesion=1,2,3,4,5,6 412 0.43468990 benign (0.5251848 0.4748152) *
##   3) Marg.adhesion=7,8,9,10 67 0.01919717 malignant (0.2271658 0.7728342) *
## 
## $trees[[146]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4613521 malignant (0.4231633 0.5768367) *
## 
## $trees[[147]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 5.000000e-01 benign (0.500000000 0.500000000)  
##   2) Epith.c.size=1,2,3,4,5,6,7 439 4.744029e-01 benign (0.513116906 0.486883094) *
##   3) Epith.c.size=8,9,10 40 3.572304e-05 malignant (0.001393645 0.998606355) *
## 
## $trees[[148]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4756855 malignant (0.4514857 0.5485143) *
## 
## $trees[[149]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Normal.nucleoli=1,2,3,4,5,6,7,8 432 0.4745167 benign (0.5130748 0.4869252) *
##   3) Normal.nucleoli=9,10 47 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[150]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4757525 malignant (0.4516189 0.5483811) *
## 
## $trees[[151]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 malignant (0.5000000 0.5000000)  
##   2) Cl.thickness=1,2,3,4,5,6,7,8 418 0.4746499 benign (0.5130047 0.4869953) *
##   3) Cl.thickness=9,10 61 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[152]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.475873100 malignant (0.45185831 0.54814169)  
##   2) Cl.thickness=1 99 0.000249881 benign (0.98721845 0.01278155) *
##   3) Cl.thickness=2,3,4,5,6,7,8,9,10 380 0.454615800 malignant (0.44068393 0.55931607) *
## 
## $trees[[153]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4807774 benign (0.5383885 0.4616115)  
##   2) Cell.shape=1,2,3,4,5,6,7,8 430 0.4579967 benign (0.5504278 0.4495722) *
##   3) Cell.shape=9,10 49 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[154]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4789848 malignant (0.4580436 0.5419564) *
## 
## $trees[[155]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 5.000000e-01 benign (0.500000000 0.500000000)  
##   2) Epith.c.size=1,2,3,4,5,6,7 439 4.764364e-01 benign (0.512049518 0.487950482) *
##   3) Epith.c.size=8,9,10 40 3.326538e-05 malignant (0.001409735 0.998590265) *
## 
## $trees[[156]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4775304 malignant (0.4551513 0.5448487) *
## 
## $trees[[157]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 malignant (0.5000000 0.5000000)  
##   2) Normal.nucleoli=1,2,3,4,5,6,7,8 432 0.4766149 benign (0.5119725 0.4880275) *
##   3) Normal.nucleoli=9,10 47 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[158]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.47765980 malignant (0.45540853 0.54459147)  
##   2) Mitoses=1,2 418 0.47425640 malignant (0.46985227 0.53014773) *
##   3) Mitoses=3,4,5,6,7,8,10 61 0.00340334 malignant (0.08619008 0.91380992) *
## 
## $trees[[159]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.500000000 malignant (0.5000000 0.5000000)  
##   2) Mitoses=1,2 418 0.468414700 benign (0.5145218 0.4854782) *
##   3) Mitoses=3,4,5,6,7,8,10 61 0.003562515 malignant (0.1013579 0.8986421) *
## 
## $trees[[160]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.473865 malignant (0.4478723 0.5521277) *
## 
## $trees[[161]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Cl.thickness=1,2,3,4,5,6,7,8 418 0.4778402 benign (0.5113310 0.4886690) *
##   3) Cl.thickness=9,10 61 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[162]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4787807 malignant (0.4576376 0.5423624)  
##   2) Bare.nuclei=1,2,3,4,5,7,8 379 0.4787807 malignant (0.4687518 0.5312482) *
##   3) Bare.nuclei=6,9,10 100 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[163]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 malignant (0.5000000 0.5000000)  
##   2) Bare.nuclei=1,2,3,4,5,7,8 379 0.4781417 benign (0.5111734 0.4888266) *
##   3) Bare.nuclei=6,9,10 100 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[164]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4790573 malignant (0.4581879 0.5418121) *
## 
## $trees[[165]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Normal.nucleoli=1,2,3,4,5,6,7,8 432 0.4783661 benign (0.5110562 0.4889438) *
##   3) Normal.nucleoli=9,10 47 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[166]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4792633 malignant (0.4585978 0.5414022) *
## 
## $trees[[167]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Bl.cromatin=1,2,3,4,5,7 437 0.4783651 benign (0.5110566 0.4889434) *
##   3) Bl.cromatin=6,8,9,10 42 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[168]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4792625 malignant (0.4585961 0.5414039) *
## 
## $trees[[169]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 5.000000e-01 benign (0.500000000 0.500000000)  
##   2) Epith.c.size=1,2,3,4,5,6,7 439 4.782261e-01 benign (0.511111026 0.488888974) *
##   3) Epith.c.size=8,9,10 40 3.651986e-05 malignant (0.001674422 0.998325578) *
## 
## $trees[[170]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4791715000 malignant (0.45841507 0.54158493)  
##   2) Bl.cromatin=1 100 0.0003253254 benign (0.98252513 0.01747487) *
##   3) Bl.cromatin=2,3,4,5,6,7,8,9,10 379 0.4592898000 malignant (0.44806870 0.55193130) *
## 
## $trees[[171]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4819581 benign (0.5360368 0.4639632)  
##   2) Epith.c.size=2,3,4,5,6,7,8,9,10 448 0.4195146 benign (0.5587201 0.4412799) *
##   3) Epith.c.size=1 31 0.0238774 malignant (0.2912890 0.7087110) *
## 
## $trees[[172]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.47083290 malignant (0.4418636 0.5581364)  
##   2) Cell.shape=1,2 286 0.08997291 benign (0.5850881 0.4149119) *
##   3) Cell.shape=3,4,5,6,7,8,9,10 193 0.32823830 malignant (0.3993911 0.6006089) *
## 
## $trees[[173]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.48502030 benign (0.5299325 0.4700675)  
##   2) Epith.c.size=2,3,4,5,6,7,8,9,10 448 0.41795740 benign (0.5552909 0.4447091) *
##   3) Epith.c.size=1 31 0.02345393 malignant (0.2707823 0.7292177) *
## 
## $trees[[174]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.46653810 malignant (0.4333745 0.5666255)  
##   2) Normal.nucleoli=2 23 0.01058392 benign (0.7676172 0.2323828) *
##   3) Normal.nucleoli=1,3,4,5,6,7,8,9,10 456 0.42656170 malignant (0.4163830 0.5836170) *
## 
## $trees[[175]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.476593500 benign (0.5467106 0.4532894)  
##   2) Mitoses=1,2 418 0.446458000 benign (0.5609188 0.4390812) *
##   3) Mitoses=3,4,5,6,7,8,10 61 0.004074857 malignant (0.1292984 0.8707016) *
## 
## $trees[[176]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.47709970 malignant (0.4542953 0.5457047)  
##   2) Marg.adhesion=1,2,3,4,5,6 412 0.45980090 malignant (0.4751590 0.5248410) *
##   3) Marg.adhesion=7,8,9,10 67 0.01729882 malignant (0.2096338 0.7903662) *
## 
## $trees[[177]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.50000000 benign (0.5000000 0.5000000)  
##   2) Marg.adhesion=1,2,3,4,5,6 412 0.44309830 benign (0.5209588 0.4790412) *
##   3) Marg.adhesion=7,8,9,10 67 0.01812915 malignant (0.2416226 0.7583774) *
## 
## $trees[[178]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.46684640 malignant (0.4339830 0.5660170)  
##   2) Epith.c.size=7,8,9,10 49 0.02158969 benign (0.6906982 0.3093018) *
##   3) Epith.c.size=1,2,3,4,5,6 430 0.41178710 malignant (0.4134368 0.5865632) *
## 
## $trees[[179]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 4.763232e-01 benign (0.5472476010 0.4527523990)  
##   2) Epith.c.size=1,2,3,4,5,6,7 439 4.516032e-01 benign (0.5604053091 0.4395946909) *
##   3) Epith.c.size=8,9,10 40 2.180598e-05 malignant (0.0009688726 0.9990311274) *
## 
## $trees[[180]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 4.774848e-01 malignant (0.4550606605 0.5449393395)  
##   2) Epith.c.size=1,2,3,4,5,6,7 439 4.774606e-01 malignant (0.4660025410 0.5339974590) *
##   3) Epith.c.size=8,9,10 40 2.414169e-05 malignant (0.0009778258 0.9990221742) *
## 
## $trees[[181]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 5.000000e-01 benign (0.500000000 0.500000000)  
##   2) Epith.c.size=1,2,3,4,5,6,7 439 4.784318e-01 benign (0.511009161 0.488990839) *
##   3) Epith.c.size=8,9,10 40 2.528006e-05 malignant (0.001170729 0.998829271) *
## 
## $trees[[182]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4793492 malignant (0.4587687 0.5412313) *
## 
## $trees[[183]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Cl.thickness=1,2,3,4,5,6,7,8 418 0.4786828 benign (0.5108908 0.4891092) *
##   3) Cl.thickness=9,10 61 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[184]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4795545 malignant (0.4591772 0.5408228) *
## 
## $trees[[185]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Bare.nuclei=1,2,3,4,5,7,8 379 0.4790111 benign (0.5107194 0.4892806) *
##   3) Bare.nuclei=6,9,10 100 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[186]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4798567 malignant (0.4597786 0.5402214)  
##   2) Normal.nucleoli=1,2,3,4,5,6,7,8 432 0.4798567 malignant (0.4700573 0.5299427) *
##   3) Normal.nucleoli=9,10 47 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[187]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Normal.nucleoli=1,2,3,4,5,6,7,8 432 0.4797611 benign (0.5103285 0.4896715) *
##   3) Normal.nucleoli=9,10 47 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[188]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4805485 malignant (0.4611557 0.5388443) *
## 
## $trees[[189]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Cell.shape=1,2,3,4,5,6,7,8 430 0.4797607 benign (0.5103287 0.4896713) *
##   3) Cell.shape=9,10 49 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[190]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4805481 malignant (0.4611549 0.5388451) *
## 
## $trees[[191]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 malignant (0.5000000 0.5000000)  
##   2) Bl.cromatin=1,2,3,4,5,7 437 0.4798265 benign (0.5102944 0.4897056) *
##   3) Bl.cromatin=6,8,9,10 42 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[192]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.48060890 malignant (0.4612761 0.5387239)  
##   2) Bare.nuclei=1,2,3,5,7,8 367 0.45797230 benign (0.4820835 0.5179165) *
##   3) Bare.nuclei=4,6,9,10 112 0.01992353 malignant (0.2308678 0.7691322) *
## 
## $trees[[193]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4620265000 malignant (0.42448862 0.57551138)  
##   2) Cl.thickness=1 99 0.0001857315 benign (0.98786816 0.01213184) *
##   3) Cl.thickness=2,3,4,5,6,7,8,9,10 380 0.4444168000 malignant (0.41510815 0.58489185) *
## 
## $trees[[194]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 4.843556e-01 benign (0.531258115 0.468741885)  
##   2) Epith.c.size=1,2,3,4,5,6,7 439 4.648083e-01 benign (0.541490799 0.458509201) *
##   3) Epith.c.size=8,9,10 40 2.283621e-05 malignant (0.001242177 0.998757823) *
## 
## $trees[[195]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4817618 malignant (0.4635721 0.5364279)  
##   2) Cl.thickness=1,2,3,4,5,6,7,8 418 0.4817618 malignant (0.4733104 0.5266896) *
##   3) Cl.thickness=9,10 61 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[196]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Cl.thickness=1,2,3,4,5,6,7,8 418 0.4808223 benign (0.5097763 0.4902237) *
##   3) Cl.thickness=9,10 61 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[197]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4815307 malignant (0.4631117 0.5368883)  
##   2) Normal.nucleoli=1,2,3,4,5,6,7,8 432 0.4815307 malignant (0.4727798 0.5272202) *
##   3) Normal.nucleoli=9,10 47 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[198]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Normal.nucleoli=1,2,3,4,5,6,7,8 432 0.4809555 benign (0.5097071 0.4902929) *
##   3) Normal.nucleoli=9,10 47 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[199]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4816543 malignant (0.4633579 0.5366421) *
## 
## $trees[[200]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.500000 benign (0.5000000 0.5000000)  
##   2) Bl.cromatin=1,2,3,4,5,7 437 0.481152 benign (0.5096051 0.4903949) *
##   3) Bl.cromatin=6,8,9,10 42 0.000000 malignant (0.0000000 1.0000000) *
## 
## 
## $weights
##   [1] 2.57171376 1.98562559 1.53214494 1.00646554 1.06984804 0.58019994
##   [7] 0.54512345 0.69174862 0.63907941 0.93493197 0.61633190 0.73738093
##  [13] 0.27416883 0.44621535 0.35442263 0.34045528 0.49082060 0.42428973
##  [19] 0.21698205 0.28573590 0.24136509 0.27664787 0.51548291 0.20395515
##  [25] 0.59280695 0.33524790 0.28585216 0.25451148 0.22565756 0.24769525
##  [31] 0.21890807 0.25537531 0.21519545 0.20224613 0.18362034 0.25376556
##  [37] 0.28877261 0.34724792 0.20579387 0.46003697 0.48403854 0.39373896
##  [43] 0.32694841 0.17265520 0.16643897 0.27691471 0.29552284 0.24188938
##  [49] 0.20470745 0.17842233 0.16262279 0.16328323 0.15093530 0.14175468
##  [55] 0.38589301 0.47117978 0.18349372 0.21686071 0.17116546 0.15764353
##  [61] 0.16427411 0.15178131 0.14105727 0.34749070 0.28151850 0.34715485
##  [67] 0.22652739 0.23132047 0.19201268 0.15830614 0.14667397 0.15103142
##  [73] 0.14040954 0.14115179 0.13183312 0.13129701 0.12319832 0.12921979
##  [79] 0.12134513 0.12706752 0.11946768 0.12403806 0.36468519 0.23019104
##  [85] 0.13853641 0.12986828 0.12193977 0.12969057 0.12178311 0.12741376
##  [91] 0.35999102 0.24907727 0.12762383 0.12448467 0.11718211 0.11558929
##  [97] 0.10926756 0.11573336 0.10939628 0.13887734 0.12764782 0.11299389
## [103] 0.20316297 0.19089730 0.10672826 0.27030309 0.17784385 0.10512612
## [109] 0.18273329 0.12627074 0.11712958 0.10524783 0.27941676 0.17894463
## [115] 0.19117953 0.15983386 0.17713181 0.15256223 0.11325275 0.10717754
## [121] 0.10891407 0.10328424 0.10374646 0.09862604 0.12573994 0.11640063
## [127] 0.10222514 0.09723852 0.09953589 0.09481350 0.09911853 0.18011771
## [133] 0.16796562 0.08258451 0.09346084 0.25396065 0.28618476 0.25190239
## [139] 0.29995944 0.35009897 0.20299337 0.09881186 0.11155498 0.10565597
## [145] 0.18497743 0.15490063 0.10233468 0.09733475 0.10202147 0.09706598
## [151] 0.10148754 0.18103001 0.16841008 0.08411053 0.09419107 0.08993918
## [157] 0.09360870 0.08942051 0.11220890 0.10463551 0.08869717 0.08492836
## [163] 0.08748885 0.08381995 0.08658979 0.08299441 0.08659347 0.08299779
## [169] 0.08700436 0.16189200 0.22740718 0.33012105 0.23543634 0.25275466
## [175] 0.19851814 0.09166521 0.15540217 0.26808715 0.19410728 0.09012188
## [181] 0.08622486 0.08265037 0.08532057 0.08182774 0.08400499 0.08061696
## [187] 0.08099981 0.07784542 0.08100149 0.07784697 0.08073764 0.08847438
## [193] 0.22250313 0.14090795 0.07298508 0.07674852 0.07391090 0.07621487
## [199] 0.07341587 0.07542793
## 
## $votes
##             [,1]     [,2]
##   [1,] 28.509864 18.20407
##   [2,] 28.551731 18.16220
##   [3,] 24.333644 22.38029
##   [4,] 14.045281 32.66865
##   [5,] 29.444796 17.26914
##   [6,] 28.981044 17.73289
##   [7,] 28.027098 18.68684
##   [8,] 22.802721 23.91121
##   [9,] 29.225567 17.48837
##  [10,] 12.903357 33.81058
##  [11,] 23.429685 23.28425
##  [12,] 28.981044 17.73289
##  [13,] 28.509864 18.20407
##  [14,] 28.509864 18.20407
##  [15,] 13.205685 33.50825
##  [16,] 16.298775 30.41516
##  [17,] 29.915976 16.79796
##  [18,] 30.118633 16.59530
##  [19,] 23.101644 23.61229
##  [20,] 28.981044 17.73289
##  [21,] 29.915976 16.79796
##  [22,] 27.824441 18.88949
##  [23,] 15.832284 30.88165
##  [24,] 28.694218 18.01972
##  [25,] 29.915976 16.79796
##  [26,] 29.915976 16.79796
##  [27,] 26.889509 19.82442
##  [28,] 22.627790 24.08614
##  [29,] 19.865676 26.84826
##  [30,] 13.251170 33.46276
##  [31,] 22.287551 24.42638
##  [32,] 12.868022 33.84591
##  [33,] 26.689438 20.02450
##  [34,] 23.011877 23.70206
##  [35,] 17.741234 28.97270
##  [36,] 16.609733 30.10420
##  [37,] 16.745900 29.96803
##  [38,] 17.968661 28.74527
##  [39,] 21.300800 25.41313
##  [40,] 19.644042 27.06989
##  [41,] 20.598892 26.11504
##  [42,] 29.696747 17.01719
##  [43,] 15.653824 31.06011
##  [44,] 22.202099 24.51183
##  [45,] 19.914115 26.79982
##  [46,] 28.509864 18.20407
##  [47,] 16.372514 30.34142
##  [48,] 19.154118 27.55982
##  [49,] 28.896681 17.81725
##  [50,] 14.525556 32.18838
##  [51,] 29.953069 16.76086
##  [52,] 25.658752 21.05518
##  [53,] 28.551731 18.16220
##  [54,] 27.824441 18.88949
##  [55,] 28.230466 18.48347
##  [56,] 28.509864 18.20407
##  [57,] 29.022911 17.69102
##  [58,] 15.803235 30.91070
##  [59,] 22.378098 24.33583
##  [60,] 28.509864 18.20407
##  [61,] 29.165398 17.54854
##  [62,] 30.118633 16.59530
##  [63,] 30.595130 16.11880
##  [64,] 30.118633 16.59530
##  [65,] 30.595130 16.11880
##  [66,] 28.509864 18.20407
##  [67,]  8.061884 38.65205
##  [68,] 17.081163 29.63277
##  [69,] 12.858179 33.85575
##  [70,] 21.966359 24.74757
##  [71,] 17.825996 28.88794
##  [72,] 31.105295 15.60864
##  [73,] 17.599925 29.11401
##  [74,] 25.879061 20.83487
##  [75,] 12.535459 34.17847
##  [76,] 25.980017 20.73392
##  [77,] 30.672998 16.04093
##  [78,] 26.240346 20.47359
##  [79,] 28.551731 18.16220
##  [80,] 27.616799 19.09713
##  [81,]  8.766268 37.94766
##  [82,] 30.589812 16.12412
##  [83,] 13.009862 33.70407
##  [84,] 18.509806 28.20413
##  [85,] 30.826115 15.88782
##  [86,] 28.058116 18.65582
##  [87,] 10.717221 35.99671
##  [88,] 32.468064 14.24587
##  [89,] 28.132282 18.58165
##  [90,] 28.981044 17.73289
##  [91,] 31.345708 15.36822
##  [92,] 31.345708 15.36822
##  [93,] 31.345708 15.36822
##  [94,] 17.003465 29.71047
##  [95,] 30.672998 16.04093
##  [96,] 29.915976 16.79796
##  [97,] 23.164175 23.54976
##  [98,] 15.972596 30.74134
##  [99,] 28.027098 18.68684
## [100,] 18.697112 28.01682
## [101,] 29.517711 17.19622
## [102,] 32.019545 14.69439
## [103,] 17.734541 28.97939
## [104,] 30.589812 16.12412
## [105,] 29.444796 17.26914
## [106,]  8.185466 38.52847
## [107,] 15.672662 31.04127
## [108,] 31.061953 15.65198
## [109,] 29.444796 17.26914
## [110,] 25.894719 20.81921
## [111,] 30.168887 16.54505
## [112,] 10.209193 36.50474
## [113,] 29.444796 17.26914
## [114,] 29.177431 17.53650
## [115,] 31.345708 15.36822
## [116,] 30.118633 16.59530
## [117,] 30.589812 16.12412
## [118,] 16.518187 30.19575
## [119,] 29.444796 17.26914
## [120,] 14.195159 32.51877
## [121,] 28.509864 18.20407
## [122,] 28.027098 18.68684
## [123,] 32.019545 14.69439
## [124,] 18.253542 28.46039
## [125,] 16.760946 29.95299
## [126,] 12.557863 34.15607
## [127,] 29.195705 17.51823
## [128,] 28.981044 17.73289
## [129,] 30.118633 16.59530
## [130,] 29.444796 17.26914
## [131,] 23.328995 23.38494
## [132,] 26.689438 20.02450
## [133,] 32.019545 14.69439
## [134,] 29.915976 16.79796
## [135,] 14.125900 32.58803
## [136,] 30.118633 16.59530
## [137,] 30.118633 16.59530
## [138,]  8.916997 37.79694
## [139,] 15.971691 30.74224
## [140,] 26.418329 20.29560
## [141,]  7.229462 39.48447
## [142,] 30.118633 16.59530
## [143,]  5.711058 41.00287
## [144,]  8.093476 38.62046
## [145,] 17.218567 29.49537
## [146,] 30.118633 16.59530
## [147,] 14.830181 31.88375
## [148,] 29.368054 17.34588
## [149,] 23.722537 22.99140
## [150,] 20.913129 25.80080
## [151,] 12.525453 34.18848
## [152,] 30.589812 16.12412
## [153,] 11.271818 35.44212
## [154,] 22.173218 24.54072
## [155,]  7.121582 39.59235
## [156,] 20.503296 26.21064
## [157,] 23.779784 22.93415
## [158,] 26.181334 20.53260
## [159,] 13.748357 32.96558
## [160,] 14.735220 31.97871
## [161,] 15.019966 31.69397
## [162,] 25.860107 20.85383
## [163,] 28.232390 18.48154
## [164,] 26.866221 19.84771
## [165,] 14.862837 31.85110
## [166,] 19.636552 27.07738
## [167,] 27.818116 18.89582
## [168,] 32.019545 14.69439
## [169,] 13.251170 33.46276
## [170,] 15.653824 31.06011
## [171,] 29.915976 16.79796
## [172,] 29.444796 17.26914
## [173,] 23.046373 23.66756
## [174,]  9.595143 37.11879
## [175,] 17.605332 29.10860
## [176,] 11.984790 34.72914
## [177,] 16.460598 30.25334
## [178,] 30.425209 16.28872
## [179,] 20.210501 26.50343
## [180,] 17.062678 29.65126
## [181,] 30.118633 16.59530
## [182,] 20.210501 26.50343
## [183,] 31.996884 14.71705
## [184,] 29.915976 16.79796
## [185,] 30.589812 16.12412
## [186,] 30.118633 16.59530
## [187,] 16.285414 30.42852
## [188,] 29.444796 17.26914
## [189,] 16.089650 30.62428
## [190,] 16.056729 30.65720
## [191,] 16.998365 29.71557
## [192,] 10.194118 36.51982
## [193,]  3.995581 42.71835
## [194,] 11.619842 35.09409
## [195,] 30.118633 16.59530
## [196,] 15.247179 31.46675
## [197,] 16.753463 29.96047
## [198,] 24.303497 22.41044
## [199,] 29.976877 16.73706
## [200,] 19.891996 26.82194
## [201,] 16.442072 30.27186
## [202,] 30.118633 16.59530
## [203,]  4.249541 42.46439
## [204,] 30.118633 16.59530
## [205,] 16.080096 30.63384
## [206,]  9.664627 37.04931
## [207,] 30.118633 16.59530
## [208,] 27.558554 19.15538
## [209,] 29.915976 16.79796
## [210,] 32.019545 14.69439
## [211,] 20.117623 26.59631
## [212,] 28.498277 18.21566
## [213,] 19.537092 27.17684
## [214,] 16.217448 30.49649
## [215,] 30.118633 16.59530
## [216,] 23.121098 23.59284
## [217,] 16.099511 30.61442
## [218,] 29.444796 17.26914
## [219,] 16.215731 30.49820
## [220,] 30.118633 16.59530
## [221,] 30.431459 16.28247
## [222,] 19.033474 27.68046
## [223,] 17.698431 29.01550
## [224,] 14.731696 31.98224
## [225,] 28.509864 18.20407
## [226,] 30.782554 15.93138
## [227,] 18.467244 28.24669
## [228,] 17.584414 29.12952
## [229,] 32.019545 14.69439
## [230,] 30.118633 16.59530
## [231,] 28.498277 18.21566
## [232,] 18.033345 28.68059
## [233,] 15.574189 31.13974
## [234,] 30.118633 16.59530
## [235,] 32.019545 14.69439
## [236,] 10.848982 35.86495
## [237,] 30.443492 16.27044
## [238,] 23.195366 23.51857
## [239,] 23.007136 23.70680
## [240,] 28.590350 18.12358
## [241,] 23.316405 23.39753
## [242,] 10.872521 35.84141
## [243,] 30.589812 16.12412
## [244,] 22.247993 24.46594
## [245,] 10.137133 36.57680
## [246,] 15.644191 31.06974
## [247,]  6.469931 40.24400
## [248,] 12.711451 34.00248
## [249,] 29.022911 17.69102
## [250,] 24.128943 22.58499
## [251,] 29.444796 17.26914
## [252,] 29.915976 16.79796
## [253,]  7.723567 38.99037
## [254,] 10.620167 36.09377
## [255,] 27.575349 19.13858
## [256,] 31.096617 15.61732
## [257,] 28.981044 17.73289
## [258,] 28.230466 18.48347
## [259,] 29.915976 16.79796
## [260,] 32.019545 14.69439
## [261,] 30.589812 16.12412
## [262,] 25.301407 21.41253
## [263,] 32.019545 14.69439
## [264,] 13.839715 32.87422
## [265,] 31.345708 15.36822
## [266,] 28.185858 18.52808
## [267,] 17.421723 29.29221
## [268,] 24.264644 22.44929
## [269,] 32.468064 14.24587
## [270,] 30.640067 16.07387
## [271,] 29.839234 16.87470
## [272,] 12.525453 34.18848
## [273,] 29.915976 16.79796
## [274,] 29.928010 16.78592
## [275,] 29.666884 17.04705
## [276,] 30.410776 16.30316
## [277,] 32.468064 14.24587
## [278,] 13.396099 33.31783
## [279,] 31.345708 15.36822
## [280,] 32.751206 13.96273
## [281,] 30.589812 16.12412
## [282,] 28.981044 17.73289
## [283,] 25.229439 21.48449
## [284,] 29.915976 16.79796
## [285,] 30.589812 16.12412
## [286,] 12.154658 34.55928
## [287,] 24.159591 22.55434
## [288,] 19.130481 27.58345
## [289,] 30.589812 16.12412
## [290,] 28.773995 17.93994
## [291,] 26.731305 19.98263
## [292,] 25.530705 21.18323
## [293,] 28.058116 18.65582
## [294,]  5.385552 41.32838
## [295,] 30.589812 16.12412
## [296,] 29.915976 16.79796
## [297,] 30.570187 16.14375
## [298,] 29.241526 17.47241
## [299,] 31.533132 15.18080
## [300,] 29.525282 17.18865
## [301,] 23.711483 23.00245
## [302,] 13.590905 33.12303
## [303,] 21.125606 25.58833
## [304,] 30.410776 16.30316
## [305,] 30.410776 16.30316
## [306,] 25.670744 21.04319
## [307,] 29.306053 17.40788
## [308,] 31.126479 15.58745
## [309,] 27.160618 19.55332
## [310,] 31.345708 15.36822
## [311,] 32.019545 14.69439
## [312,] 30.410776 16.30316
## [313,] 29.928010 16.78592
## [314,] 11.059473 35.65446
## [315,] 25.069083 21.64485
## [316,] 29.525282 17.18865
## [317,] 11.050282 35.66365
## [318,] 29.999162 16.71477
## [319,] 28.590350 18.12358
## [320,] 32.962865 13.75107
## [321,] 30.410776 16.30316
## [322,] 16.499751 30.21418
## [323,] 30.410776 16.30316
## [324,] 30.589812 16.12412
## [325,] 28.319241 18.39469
## [326,] 30.410776 16.30316
## [327,] 30.410776 16.30316
## [328,] 31.345708 15.36822
## [329,] 30.410776 16.30316
## [330,] 30.410776 16.30316
## [331,] 16.469692 30.24424
## [332,] 28.319241 18.39469
## [333,]  7.395799 39.31813
## [334,] 14.311944 32.40199
## [335,] 27.214518 19.49942
## [336,] 27.824441 18.88949
## [337,]  6.395578 40.31835
## [338,] 22.070589 24.64334
## [339,] 23.235763 23.47817
## [340,] 32.019545 14.69439
## [341,] 16.255381 30.45855
## [342,] 26.889509 19.82442
## [343,] 12.259250 34.45468
## [344,] 24.078042 22.63589
## [345,] 27.824441 18.88949
## [346,] 28.981044 17.73289
## [347,] 28.981044 17.73289
## [348,] 28.509864 18.20407
## [349,] 28.230466 18.48347
## [350,] 28.509864 18.20407
## [351,] 32.019545 14.69439
## [352,] 16.887211 29.82672
## [353,] 29.099938 17.61400
## [354,] 30.410776 16.30316
## [355,] 31.345708 15.36822
## [356,] 28.981044 17.73289
## [357,] 28.498277 18.21566
## [358,] 16.952086 29.76185
## [359,] 32.019545 14.69439
## [360,] 15.190300 31.52363
## [361,] 30.595130 16.11880
## [362,] 30.410776 16.30316
## [363,] 28.509864 18.20407
## [364,] 28.737270 17.97666
## [365,] 26.889509 19.82442
## [366,] 29.915976 16.79796
## [367,] 28.445126 18.26881
## [368,] 28.509864 18.20407
## [369,] 28.509864 18.20407
## [370,] 28.981044 17.73289
## [371,] 28.981044 17.73289
## [372,] 28.087979 18.62595
## [373,] 31.345708 15.36822
## [374,] 27.839063 18.87487
## [375,] 28.981044 17.73289
## [376,] 28.242470 18.47146
## [377,] 29.254173 17.45976
## [378,] 29.254173 17.45976
## [379,] 29.915976 16.79796
## [380,] 30.118633 16.59530
## [381,] 31.463525 15.25041
## [382,] 27.939220 18.77471
## [383,] 29.915976 16.79796
## [384,] 28.509864 18.20407
## [385,] 28.509864 18.20407
## [386,] 29.915976 16.79796
## [387,] 11.059473 35.65446
## [388,] 19.592248 27.12168
## [389,] 15.650942 31.06299
## [390,] 16.870380 29.84355
## [391,] 12.011505 34.70243
## [392,] 29.915976 16.79796
## [393,] 21.536913 25.17702
## [394,] 28.981044 17.73289
## [395,] 30.589812 16.12412
## [396,] 30.118633 16.59530
## [397,] 16.255381 30.45855
## [398,] 13.739378 32.97456
## [399,] 31.345708 15.36822
## [400,] 28.590350 18.12358
## [401,] 32.019545 14.69439
## [402,] 17.595080 29.11885
## [403,] 30.410776 16.30316
## [404,] 21.692060 25.02187
## [405,] 18.448905 28.26503
## [406,] 30.410776 16.30316
## [407,] 18.205721 28.50821
## [408,] 28.981044 17.73289
## [409,] 29.915976 16.79796
## [410,] 27.682498 19.03144
## [411,] 29.915976 16.79796
## [412,] 28.498277 18.21566
## [413,] 28.981044 17.73289
## [414,] 18.756253 27.95768
## [415,] 15.852800 30.86113
## [416,] 29.660198 17.05374
## [417,] 32.019545 14.69439
## [418,] 30.410776 16.30316
## [419,] 12.709482 34.00445
## [420,] 17.112624 29.60131
## [421,] 27.344262 19.36967
## [422,] 27.824441 18.88949
## [423,] 28.058116 18.65582
## [424,] 28.981044 17.73289
## [425,] 28.981044 17.73289
## [426,] 29.915976 16.79796
## [427,] 26.376620 20.33731
## [428,] 32.019545 14.69439
## [429,] 26.138931 20.57500
## [430,] 27.503174 19.21076
## [431,] 31.345708 15.36822
## [432,] 28.981044 17.73289
## [433,] 32.019545 14.69439
## [434,] 19.408077 27.30586
## [435,] 30.708965 16.00497
## [436,] 12.154203 34.55973
## [437,] 25.630809 21.08312
## [438,] 30.410776 16.30316
## [439,] 28.590350 18.12358
## [440,] 29.915976 16.79796
## [441,] 32.019545 14.69439
## [442,] 31.345708 15.36822
## [443,] 30.199118 16.51481
## [444,] 11.323305 35.39063
## [445,] 29.915976 16.79796
## [446,] 27.675524 19.03841
## [447,] 28.769386 17.94455
## [448,] 28.981044 17.73289
## [449,] 29.444796 17.26914
## [450,] 29.915976 16.79796
## [451,] 28.981044 17.73289
## [452,] 15.848135 30.86580
## [453,] 30.589812 16.12412
## [454,] 28.509864 18.20407
## [455,] 29.666884 17.04705
## [456,] 28.095550 18.61838
## [457,] 29.660198 17.05374
## [458,] 29.444796 17.26914
## [459,] 15.706536 31.00740
## [460,] 28.694218 18.01972
## [461,] 29.444796 17.26914
## [462,] 27.839063 18.87487
## [463,] 30.589812 16.12412
## [464,] 29.774033 16.93990
## [465,] 30.589812 16.12412
## [466,] 32.019545 14.69439
## [467,] 31.345708 15.36822
## [468,]  4.189336 42.52460
## [469,]  9.420614 37.29332
## [470,] 32.019545 14.69439
## [471,] 32.019545 14.69439
## [472,] 30.431459 16.28247
## [473,] 30.410776 16.30316
## [474,] 29.755638 16.95830
## [475,] 19.678619 27.03531
## [476,] 31.345708 15.36822
## [477,] 29.915976 16.79796
## [478,] 16.910498 29.80343
## [479,] 21.635751 25.07818
## 
## $prob
##              [,1]      [,2]
##   [1,] 0.61030751 0.3896925
##   [2,] 0.61120374 0.3887963
##   [3,] 0.52090762 0.4790924
##   [4,] 0.30066578 0.6993342
##   [5,] 0.63032150 0.3696785
##   [6,] 0.62039401 0.3796060
##   [7,] 0.59997298 0.4000270
##   [8,] 0.48813533 0.5118647
##   [9,] 0.62562848 0.3743715
##  [10,] 0.27622074 0.7237793
##  [11,] 0.50155668 0.4984433
##  [12,] 0.62039401 0.3796060
##  [13,] 0.61030751 0.3896925
##  [14,] 0.61030751 0.3896925
##  [15,] 0.28269265 0.7173074
##  [16,] 0.34890607 0.6510939
##  [17,] 0.64040799 0.3595920
##  [18,] 0.64474624 0.3552538
##  [19,] 0.49453433 0.5054657
##  [20,] 0.62039401 0.3796060
##  [21,] 0.64040799 0.3595920
##  [22,] 0.59563473 0.4043653
##  [23,] 0.33891996 0.6610800
##  [24,] 0.61425395 0.3857460
##  [25,] 0.64040799 0.3595920
##  [26,] 0.64040799 0.3595920
##  [27,] 0.57562075 0.4243793
##  [28,] 0.48439060 0.5156094
##  [29,] 0.42526233 0.5747377
##  [30,] 0.28366633 0.7163337
##  [31,] 0.47710714 0.5228929
##  [32,] 0.27546431 0.7245357
##  [33,] 0.57133785 0.4286622
##  [34,] 0.49261270 0.5073873
##  [35,] 0.37978463 0.6202154
##  [36,] 0.35556271 0.6444373
##  [37,] 0.35847764 0.6415224
##  [38,] 0.38465314 0.6153469
##  [39,] 0.45598387 0.5440161
##  [40,] 0.42051783 0.5794822
##  [41,] 0.44095820 0.5590418
##  [42,] 0.63571497 0.3642850
##  [43,] 0.33509968 0.6649003
##  [44,] 0.47527788 0.5247221
##  [45,] 0.42629926 0.5737007
##  [46,] 0.61030751 0.3896925
##  [47,] 0.35048460 0.6495154
##  [48,] 0.41003008 0.5899699
##  [49,] 0.61858804 0.3814120
##  [50,] 0.31094698 0.6890530
##  [51,] 0.64120204 0.3587980
##  [52,] 0.54927407 0.4507259
##  [53,] 0.61120374 0.3887963
##  [54,] 0.59563473 0.4043653
##  [55,] 0.60432646 0.3956735
##  [56,] 0.61030751 0.3896925
##  [57,] 0.62129024 0.3787098
##  [58,] 0.33829811 0.6617019
##  [59,] 0.47904548 0.5209545
##  [60,] 0.61030751 0.3896925
##  [61,] 0.62434045 0.3756596
##  [62,] 0.64474624 0.3552538
##  [63,] 0.65494657 0.3450534
##  [64,] 0.64474624 0.3552538
##  [65,] 0.65494657 0.3450534
##  [66,] 0.61030751 0.3896925
##  [67,] 0.17257987 0.8274201
##  [68,] 0.36565457 0.6343454
##  [69,] 0.27525361 0.7247464
##  [70,] 0.47023142 0.5297686
##  [71,] 0.38159914 0.6184009
##  [72,] 0.66586761 0.3341324
##  [73,] 0.37675966 0.6232403
##  [74,] 0.55399021 0.4460098
##  [75,] 0.26834519 0.7316548
##  [76,] 0.55615135 0.4438486
##  [77,] 0.65661348 0.3433865
##  [78,] 0.56172419 0.4382758
##  [79,] 0.61120374 0.3887963
##  [80,] 0.59118976 0.4088102
##  [81,] 0.18765853 0.8123415
##  [82,] 0.65483273 0.3451673
##  [83,] 0.27850069 0.7214993
##  [84,] 0.39623737 0.6037626
##  [85,] 0.65989123 0.3401088
##  [86,] 0.60063699 0.3993630
##  [87,] 0.22942237 0.7705776
##  [88,] 0.69504026 0.3049597
##  [89,] 0.60222464 0.3977754
##  [90,] 0.62039401 0.3796060
##  [91,] 0.67101411 0.3289859
##  [92,] 0.67101411 0.3289859
##  [93,] 0.67101411 0.3289859
##  [94,] 0.36399130 0.6360087
##  [95,] 0.65661348 0.3433865
##  [96,] 0.64040799 0.3595920
##  [97,] 0.49587293 0.5041271
##  [98,] 0.34192361 0.6580764
##  [99,] 0.59997298 0.4000270
## [100,] 0.40024700 0.5997530
## [101,] 0.63188237 0.3681176
## [102,] 0.68543885 0.3145612
## [103,] 0.37964137 0.6203586
## [104,] 0.65483273 0.3451673
## [105,] 0.63032150 0.3696785
## [106,] 0.17522537 0.8247746
## [107,] 0.33550295 0.6644971
## [108,] 0.66493978 0.3350602
## [109,] 0.63032150 0.3696785
## [110,] 0.55432539 0.4456746
## [111,] 0.64582203 0.3541780
## [112,] 0.21854706 0.7814529
## [113,] 0.63032150 0.3696785
## [114,] 0.62459805 0.3754020
## [115,] 0.67101411 0.3289859
## [116,] 0.64474624 0.3552538
## [117,] 0.65483273 0.3451673
## [118,] 0.35360300 0.6463970
## [119,] 0.63032150 0.3696785
## [120,] 0.30387421 0.6961258
## [121,] 0.61030751 0.3896925
## [122,] 0.59997298 0.4000270
## [123,] 0.68543885 0.3145612
## [124,] 0.39075156 0.6092484
## [125,] 0.35879972 0.6412003
## [126,] 0.26882478 0.7311752
## [127,] 0.62498922 0.3750108
## [128,] 0.62039401 0.3796060
## [129,] 0.64474624 0.3552538
## [130,] 0.63032150 0.3696785
## [131,] 0.49940121 0.5005988
## [132,] 0.57133785 0.4286622
## [133,] 0.68543885 0.3145612
## [134,] 0.64040799 0.3595920
## [135,] 0.30239158 0.6976084
## [136,] 0.64474624 0.3552538
## [137,] 0.64474624 0.3552538
## [138,] 0.19088516 0.8091148
## [139,] 0.34190422 0.6580958
## [140,] 0.56553425 0.4344657
## [141,] 0.15476030 0.8452397
## [142,] 0.64474624 0.3552538
## [143,] 0.12225599 0.8777440
## [144,] 0.17325615 0.8267438
## [145,] 0.36859596 0.6314040
## [146,] 0.64474624 0.3552538
## [147,] 0.31746805 0.6825320
## [148,] 0.62867869 0.3713213
## [149,] 0.50782572 0.4921743
## [150,] 0.44768504 0.5523150
## [151,] 0.26813099 0.7318690
## [152,] 0.65483273 0.3451673
## [153,] 0.24129456 0.7587054
## [154,] 0.47465963 0.5253404
## [155,] 0.15245092 0.8475491
## [156,] 0.43891180 0.5610882
## [157,] 0.50905121 0.4909488
## [158,] 0.56046092 0.4395391
## [159,] 0.29430957 0.7056904
## [160,] 0.31543523 0.6845648
## [161,] 0.32153074 0.6784693
## [162,] 0.55358446 0.4464155
## [163,] 0.60436765 0.3956324
## [164,] 0.57512221 0.4248778
## [165,] 0.31816711 0.6818329
## [166,] 0.42035749 0.5796425
## [167,] 0.59549933 0.4045007
## [168,] 0.68543885 0.3145612
## [169,] 0.28366633 0.7163337
## [170,] 0.33509968 0.6649003
## [171,] 0.64040799 0.3595920
## [172,] 0.63032150 0.3696785
## [173,] 0.49335117 0.5066488
## [174,] 0.20540217 0.7945978
## [175,] 0.37687540 0.6231246
## [176,] 0.25655707 0.7434429
## [177,] 0.35237020 0.6476298
## [178,] 0.65130909 0.3486909
## [179,] 0.43264397 0.5673560
## [180,] 0.36525886 0.6347411
## [181,] 0.64474624 0.3552538
## [182,] 0.43264397 0.5673560
## [183,] 0.68495377 0.3150462
## [184,] 0.64040799 0.3595920
## [185,] 0.65483273 0.3451673
## [186,] 0.64474624 0.3552538
## [187,] 0.34862006 0.6513799
## [188,] 0.63032150 0.3696785
## [189,] 0.34442936 0.6555706
## [190,] 0.34372462 0.6562754
## [191,] 0.36388211 0.6361179
## [192,] 0.21822435 0.7817756
## [193,] 0.08553295 0.9144670
## [194,] 0.24874467 0.7512553
## [195,] 0.64474624 0.3552538
## [196,] 0.32639467 0.6736053
## [197,] 0.35863953 0.6413605
## [198,] 0.52026227 0.4797377
## [199,] 0.64171170 0.3582883
## [200,] 0.42582575 0.5741742
## [201,] 0.35197362 0.6480264
## [202,] 0.64474624 0.3552538
## [203,] 0.09096946 0.9090305
## [204,] 0.64474624 0.3552538
## [205,] 0.34422483 0.6557752
## [206,] 0.20688960 0.7931104
## [207,] 0.64474624 0.3552538
## [208,] 0.58994291 0.4100571
## [209,] 0.64040799 0.3595920
## [210,] 0.68543885 0.3145612
## [211,] 0.43065572 0.5693443
## [212,] 0.61005947 0.3899405
## [213,] 0.41822836 0.5817716
## [214,] 0.34716512 0.6528349
## [215,] 0.64474624 0.3552538
## [216,] 0.49495078 0.5050492
## [217,] 0.34464046 0.6553595
## [218,] 0.63032150 0.3696785
## [219,] 0.34712836 0.6528716
## [220,] 0.64474624 0.3552538
## [221,] 0.65144288 0.3485571
## [222,] 0.40744747 0.5925525
## [223,] 0.37886835 0.6211317
## [224,] 0.31535978 0.6846402
## [225,] 0.61030751 0.3896925
## [226,] 0.65895873 0.3410413
## [227,] 0.39532624 0.6046738
## [228,] 0.37642760 0.6235724
## [229,] 0.68543885 0.3145612
## [230,] 0.64474624 0.3552538
## [231,] 0.61005947 0.3899405
## [232,] 0.38603781 0.6139622
## [233,] 0.33339494 0.6666051
## [234,] 0.64474624 0.3552538
## [235,] 0.68543885 0.3145612
## [236,] 0.23224296 0.7677570
## [237,] 0.65170048 0.3482995
## [238,] 0.49654064 0.5034594
## [239,] 0.49251122 0.5074888
## [240,] 0.61203046 0.3879695
## [241,] 0.49913171 0.5008683
## [242,] 0.23274684 0.7672532
## [243,] 0.65483273 0.3451673
## [244,] 0.47626034 0.5237397
## [245,] 0.21700449 0.7829955
## [246,] 0.33489348 0.6651065
## [247,] 0.13850110 0.8614989
## [248,] 0.27211262 0.7278874
## [249,] 0.62129024 0.3787098
## [250,] 0.51652562 0.4834744
## [251,] 0.63032150 0.3696785
## [252,] 0.64040799 0.3595920
## [253,] 0.16533754 0.8346625
## [254,] 0.22734474 0.7726553
## [255,] 0.59030245 0.4096975
## [256,] 0.66568183 0.3343182
## [257,] 0.62039401 0.3796060
## [258,] 0.60432646 0.3956735
## [259,] 0.64040799 0.3595920
## [260,] 0.68543885 0.3145612
## [261,] 0.65483273 0.3451673
## [262,] 0.54162443 0.4583756
## [263,] 0.68543885 0.3145612
## [264,] 0.29626525 0.7037347
## [265,] 0.67101411 0.3289859
## [266,] 0.60337155 0.3966285
## [267,] 0.37294490 0.6270551
## [268,] 0.51943056 0.4805694
## [269,] 0.69504026 0.3049597
## [270,] 0.65590852 0.3440915
## [271,] 0.63876518 0.3612348
## [272,] 0.26813099 0.7318690
## [273,] 0.64040799 0.3595920
## [274,] 0.64066559 0.3593344
## [275,] 0.63507571 0.3649243
## [276,] 0.65100013 0.3489999
## [277,] 0.69504026 0.3049597
## [278,] 0.28676881 0.7132312
## [279,] 0.67101411 0.3289859
## [280,] 0.70110145 0.2988985
## [281,] 0.65483273 0.3451673
## [282,] 0.62039401 0.3796060
## [283,] 0.54008381 0.4599162
## [284,] 0.64040799 0.3595920
## [285,] 0.65483273 0.3451673
## [286,] 0.26019341 0.7398066
## [287,] 0.51718169 0.4828183
## [288,] 0.40952408 0.5904759
## [289,] 0.65483273 0.3451673
## [290,] 0.61596172 0.3840383
## [291,] 0.57223408 0.4277659
## [292,] 0.54653299 0.4534670
## [293,] 0.60063699 0.3993630
## [294,] 0.11528792 0.8847121
## [295,] 0.65483273 0.3451673
## [296,] 0.64040799 0.3595920
## [297,] 0.65441261 0.3455874
## [298,] 0.62597012 0.3740299
## [299,] 0.67502628 0.3249737
## [300,] 0.63204445 0.3679556
## [301,] 0.50758910 0.4924109
## [302,] 0.29093901 0.7090610
## [303,] 0.45223352 0.5477665
## [304,] 0.65100013 0.3489999
## [305,] 0.65100013 0.3489999
## [306,] 0.54953077 0.4504692
## [307,] 0.62735143 0.3726486
## [308,] 0.66632109 0.3336789
## [309,] 0.58142434 0.4185757
## [310,] 0.67101411 0.3289859
## [311,] 0.68543885 0.3145612
## [312,] 0.65100013 0.3489999
## [313,] 0.64066559 0.3593344
## [314,] 0.23674892 0.7632511
## [315,] 0.53665108 0.4633489
## [316,] 0.63204445 0.3679556
## [317,] 0.23655217 0.7634478
## [318,] 0.64218875 0.3578113
## [319,] 0.61203046 0.3879695
## [320,] 0.70563239 0.2943676
## [321,] 0.65100013 0.3489999
## [322,] 0.35320835 0.6467916
## [323,] 0.65100013 0.3489999
## [324,] 0.65483273 0.3451673
## [325,] 0.60622687 0.3937731
## [326,] 0.65100013 0.3489999
## [327,] 0.65100013 0.3489999
## [328,] 0.67101411 0.3289859
## [329,] 0.65100013 0.3489999
## [330,] 0.65100013 0.3489999
## [331,] 0.35256487 0.6474351
## [332,] 0.60622687 0.3937731
## [333,] 0.15832106 0.8416789
## [334,] 0.30637421 0.6936258
## [335,] 0.58257817 0.4174218
## [336,] 0.59563473 0.4043653
## [337,] 0.13690944 0.8630906
## [338,] 0.47246266 0.5275373
## [339,] 0.49740541 0.5025946
## [340,] 0.68543885 0.3145612
## [341,] 0.34797714 0.6520229
## [342,] 0.57562075 0.4243793
## [343,] 0.26243242 0.7375676
## [344,] 0.51543598 0.4845640
## [345,] 0.59563473 0.4043653
## [346,] 0.62039401 0.3796060
## [347,] 0.62039401 0.3796060
## [348,] 0.61030751 0.3896925
## [349,] 0.60432646 0.3956735
## [350,] 0.61030751 0.3896925
## [351,] 0.68543885 0.3145612
## [352,] 0.36150265 0.6384973
## [353,] 0.62293915 0.3770608
## [354,] 0.65100013 0.3489999
## [355,] 0.67101411 0.3289859
## [356,] 0.62039401 0.3796060
## [357,] 0.61005947 0.3899405
## [358,] 0.36289143 0.6371086
## [359,] 0.68543885 0.3145612
## [360,] 0.32517708 0.6748229
## [361,] 0.65494657 0.3450534
## [362,] 0.65100013 0.3489999
## [363,] 0.61030751 0.3896925
## [364,] 0.61517556 0.3848244
## [365,] 0.57562075 0.4243793
## [366,] 0.64040799 0.3595920
## [367,] 0.60892167 0.3910783
## [368,] 0.61030751 0.3896925
## [369,] 0.61030751 0.3896925
## [370,] 0.62039401 0.3796060
## [371,] 0.62039401 0.3796060
## [372,] 0.60127625 0.3987237
## [373,] 0.67101411 0.3289859
## [374,] 0.59594773 0.4040523
## [375,] 0.62039401 0.3796060
## [376,] 0.60458343 0.3954166
## [377,] 0.62624085 0.3737591
## [378,] 0.62624085 0.3737591
## [379,] 0.64040799 0.3595920
## [380,] 0.64474624 0.3552538
## [381,] 0.67353621 0.3264638
## [382,] 0.59809180 0.4019082
## [383,] 0.64040799 0.3595920
## [384,] 0.61030751 0.3896925
## [385,] 0.61030751 0.3896925
## [386,] 0.64040799 0.3595920
## [387,] 0.23674892 0.7632511
## [388,] 0.41940910 0.5805909
## [389,] 0.33503798 0.6649620
## [390,] 0.36114235 0.6388576
## [391,] 0.25712895 0.7428710
## [392,] 0.64040799 0.3595920
## [393,] 0.46103831 0.5389617
## [394,] 0.62039401 0.3796060
## [395,] 0.65483273 0.3451673
## [396,] 0.64474624 0.3552538
## [397,] 0.34797714 0.6520229
## [398,] 0.29411734 0.7058827
## [399,] 0.67101411 0.3289859
## [400,] 0.61203046 0.3879695
## [401,] 0.68543885 0.3145612
## [402,] 0.37665593 0.6233441
## [403,] 0.65100013 0.3489999
## [404,] 0.46435953 0.5356405
## [405,] 0.39493366 0.6050663
## [406,] 0.65100013 0.3489999
## [407,] 0.38972786 0.6102721
## [408,] 0.62039401 0.3796060
## [409,] 0.64040799 0.3595920
## [410,] 0.59259618 0.4074038
## [411,] 0.64040799 0.3595920
## [412,] 0.61005947 0.3899405
## [413,] 0.62039401 0.3796060
## [414,] 0.40151304 0.5984870
## [415,] 0.33935913 0.6606409
## [416,] 0.63493258 0.3650674
## [417,] 0.68543885 0.3145612
## [418,] 0.65100013 0.3489999
## [419,] 0.27207047 0.7279295
## [420,] 0.36632806 0.6336719
## [421,] 0.58535560 0.4146444
## [422,] 0.59563473 0.4043653
## [423,] 0.60063699 0.3993630
## [424,] 0.62039401 0.3796060
## [425,] 0.62039401 0.3796060
## [426,] 0.64040799 0.3595920
## [427,] 0.56464138 0.4353586
## [428,] 0.68543885 0.3145612
## [429,] 0.55955320 0.4404468
## [430,] 0.58875740 0.4112426
## [431,] 0.67101411 0.3289859
## [432,] 0.62039401 0.3796060
## [433,] 0.68543885 0.3145612
## [434,] 0.41546656 0.5845334
## [435,] 0.65738342 0.3426166
## [436,] 0.26018368 0.7398163
## [437,] 0.54867590 0.4513241
## [438,] 0.65100013 0.3489999
## [439,] 0.61203046 0.3879695
## [440,] 0.64040799 0.3595920
## [441,] 0.68543885 0.3145612
## [442,] 0.67101411 0.3289859
## [443,] 0.64646919 0.3535308
## [444,] 0.24239674 0.7576033
## [445,] 0.64040799 0.3595920
## [446,] 0.59244687 0.4075531
## [447,] 0.61586307 0.3841369
## [448,] 0.62039401 0.3796060
## [449,] 0.63032150 0.3696785
## [450,] 0.64040799 0.3595920
## [451,] 0.62039401 0.3796060
## [452,] 0.33925928 0.6607407
## [453,] 0.65483273 0.3451673
## [454,] 0.61030751 0.3896925
## [455,] 0.63507571 0.3649243
## [456,] 0.60143833 0.3985617
## [457,] 0.63493258 0.3650674
## [458,] 0.63032150 0.3696785
## [459,] 0.33622809 0.6637719
## [460,] 0.61425395 0.3857460
## [461,] 0.63032150 0.3696785
## [462,] 0.59594773 0.4040523
## [463,] 0.65483273 0.3451673
## [464,] 0.63736943 0.3626306
## [465,] 0.65483273 0.3451673
## [466,] 0.68543885 0.3145612
## [467,] 0.67101411 0.3289859
## [468,] 0.08968065 0.9103194
## [469,] 0.20166605 0.7983340
## [470,] 0.68543885 0.3145612
## [471,] 0.68543885 0.3145612
## [472,] 0.65144288 0.3485571
## [473,] 0.65100013 0.3489999
## [474,] 0.63697565 0.3630243
## [475,] 0.42125803 0.5787420
## [476,] 0.67101411 0.3289859
## [477,] 0.64040799 0.3595920
## [478,] 0.36200117 0.6379988
## [479,] 0.46315412 0.5368459
## 
## $class
##   [1] "benign"    "benign"    "benign"    "malignant" "benign"    "benign"   
##   [7] "benign"    "malignant" "benign"    "malignant" "benign"    "benign"   
##  [13] "benign"    "benign"    "malignant" "malignant" "benign"    "benign"   
##  [19] "malignant" "benign"    "benign"    "benign"    "malignant" "benign"   
##  [25] "benign"    "benign"    "benign"    "malignant" "malignant" "malignant"
##  [31] "malignant" "malignant" "benign"    "malignant" "malignant" "malignant"
##  [37] "malignant" "malignant" "malignant" "malignant" "malignant" "benign"   
##  [43] "malignant" "malignant" "malignant" "benign"    "malignant" "malignant"
##  [49] "benign"    "malignant" "benign"    "benign"    "benign"    "benign"   
##  [55] "benign"    "benign"    "benign"    "malignant" "malignant" "benign"   
##  [61] "benign"    "benign"    "benign"    "benign"    "benign"    "benign"   
##  [67] "malignant" "malignant" "malignant" "malignant" "malignant" "benign"   
##  [73] "malignant" "benign"    "malignant" "benign"    "benign"    "benign"   
##  [79] "benign"    "benign"    "malignant" "benign"    "malignant" "malignant"
##  [85] "benign"    "benign"    "malignant" "benign"    "benign"    "benign"   
##  [91] "benign"    "benign"    "benign"    "malignant" "benign"    "benign"   
##  [97] "malignant" "malignant" "benign"    "malignant" "benign"    "benign"   
## [103] "malignant" "benign"    "benign"    "malignant" "malignant" "benign"   
## [109] "benign"    "benign"    "benign"    "malignant" "benign"    "benign"   
## [115] "benign"    "benign"    "benign"    "malignant" "benign"    "malignant"
## [121] "benign"    "benign"    "benign"    "malignant" "malignant" "malignant"
## [127] "benign"    "benign"    "benign"    "benign"    "malignant" "benign"   
## [133] "benign"    "benign"    "malignant" "benign"    "benign"    "malignant"
## [139] "malignant" "benign"    "malignant" "benign"    "malignant" "malignant"
## [145] "malignant" "benign"    "malignant" "benign"    "benign"    "malignant"
## [151] "malignant" "benign"    "malignant" "malignant" "malignant" "malignant"
## [157] "benign"    "benign"    "malignant" "malignant" "malignant" "benign"   
## [163] "benign"    "benign"    "malignant" "malignant" "benign"    "benign"   
## [169] "malignant" "malignant" "benign"    "benign"    "malignant" "malignant"
## [175] "malignant" "malignant" "malignant" "benign"    "malignant" "malignant"
## [181] "benign"    "malignant" "benign"    "benign"    "benign"    "benign"   
## [187] "malignant" "benign"    "malignant" "malignant" "malignant" "malignant"
## [193] "malignant" "malignant" "benign"    "malignant" "malignant" "benign"   
## [199] "benign"    "malignant" "malignant" "benign"    "malignant" "benign"   
## [205] "malignant" "malignant" "benign"    "benign"    "benign"    "benign"   
## [211] "malignant" "benign"    "malignant" "malignant" "benign"    "malignant"
## [217] "malignant" "benign"    "malignant" "benign"    "benign"    "malignant"
## [223] "malignant" "malignant" "benign"    "benign"    "malignant" "malignant"
## [229] "benign"    "benign"    "benign"    "malignant" "malignant" "benign"   
## [235] "benign"    "malignant" "benign"    "malignant" "malignant" "benign"   
## [241] "malignant" "malignant" "benign"    "malignant" "malignant" "malignant"
## [247] "malignant" "malignant" "benign"    "benign"    "benign"    "benign"   
## [253] "malignant" "malignant" "benign"    "benign"    "benign"    "benign"   
## [259] "benign"    "benign"    "benign"    "benign"    "benign"    "malignant"
## [265] "benign"    "benign"    "malignant" "benign"    "benign"    "benign"   
## [271] "benign"    "malignant" "benign"    "benign"    "benign"    "benign"   
## [277] "benign"    "malignant" "benign"    "benign"    "benign"    "benign"   
## [283] "benign"    "benign"    "benign"    "malignant" "benign"    "malignant"
## [289] "benign"    "benign"    "benign"    "benign"    "benign"    "malignant"
## [295] "benign"    "benign"    "benign"    "benign"    "benign"    "benign"   
## [301] "benign"    "malignant" "malignant" "benign"    "benign"    "benign"   
## [307] "benign"    "benign"    "benign"    "benign"    "benign"    "benign"   
## [313] "benign"    "malignant" "benign"    "benign"    "malignant" "benign"   
## [319] "benign"    "benign"    "benign"    "malignant" "benign"    "benign"   
## [325] "benign"    "benign"    "benign"    "benign"    "benign"    "benign"   
## [331] "malignant" "benign"    "malignant" "malignant" "benign"    "benign"   
## [337] "malignant" "malignant" "malignant" "benign"    "malignant" "benign"   
## [343] "malignant" "benign"    "benign"    "benign"    "benign"    "benign"   
## [349] "benign"    "benign"    "benign"    "malignant" "benign"    "benign"   
## [355] "benign"    "benign"    "benign"    "malignant" "benign"    "malignant"
## [361] "benign"    "benign"    "benign"    "benign"    "benign"    "benign"   
## [367] "benign"    "benign"    "benign"    "benign"    "benign"    "benign"   
## [373] "benign"    "benign"    "benign"    "benign"    "benign"    "benign"   
## [379] "benign"    "benign"    "benign"    "benign"    "benign"    "benign"   
## [385] "benign"    "benign"    "malignant" "malignant" "malignant" "malignant"
## [391] "malignant" "benign"    "malignant" "benign"    "benign"    "benign"   
## [397] "malignant" "malignant" "benign"    "benign"    "benign"    "malignant"
## [403] "benign"    "malignant" "malignant" "benign"    "malignant" "benign"   
## [409] "benign"    "benign"    "benign"    "benign"    "benign"    "malignant"
## [415] "malignant" "benign"    "benign"    "benign"    "malignant" "malignant"
## [421] "benign"    "benign"    "benign"    "benign"    "benign"    "benign"   
## [427] "benign"    "benign"    "benign"    "benign"    "benign"    "benign"   
## [433] "benign"    "malignant" "benign"    "malignant" "benign"    "benign"   
## [439] "benign"    "benign"    "benign"    "benign"    "benign"    "malignant"
## [445] "benign"    "benign"    "benign"    "benign"    "benign"    "benign"   
## [451] "benign"    "malignant" "benign"    "benign"    "benign"    "benign"   
## [457] "benign"    "benign"    "malignant" "benign"    "benign"    "benign"   
## [463] "benign"    "benign"    "benign"    "benign"    "benign"    "malignant"
## [469] "malignant" "benign"    "benign"    "benign"    "benign"    "benign"   
## [475] "malignant" "benign"    "benign"    "malignant" "malignant"
## 
## $importance
##     Bare.nuclei     Bl.cromatin      Cell.shape       Cell.size    Cl.thickness 
##       28.171016        2.506192        2.740277       37.360946        9.835175 
##    Epith.c.size   Marg.adhesion         Mitoses Normal.nucleoli 
##        2.965481        7.317661        3.170586        5.932666 
## 
## $terms
## Class ~ Cl.thickness + Cell.size + Cell.shape + Marg.adhesion + 
##     Epith.c.size + Bare.nuclei + Bl.cromatin + Normal.nucleoli + 
##     Mitoses
## attr(,"variables")
## list(Class, Cl.thickness, Cell.size, Cell.shape, Marg.adhesion, 
##     Epith.c.size, Bare.nuclei, Bl.cromatin, Normal.nucleoli, 
##     Mitoses)
## attr(,"factors")
##                 Cl.thickness Cell.size Cell.shape Marg.adhesion Epith.c.size
## Class                      0         0          0             0            0
## Cl.thickness               1         0          0             0            0
## Cell.size                  0         1          0             0            0
## Cell.shape                 0         0          1             0            0
## Marg.adhesion              0         0          0             1            0
## Epith.c.size               0         0          0             0            1
## Bare.nuclei                0         0          0             0            0
## Bl.cromatin                0         0          0             0            0
## Normal.nucleoli            0         0          0             0            0
## Mitoses                    0         0          0             0            0
##                 Bare.nuclei Bl.cromatin Normal.nucleoli Mitoses
## Class                     0           0               0       0
## Cl.thickness              0           0               0       0
## Cell.size                 0           0               0       0
## Cell.shape                0           0               0       0
## Marg.adhesion             0           0               0       0
## Epith.c.size              0           0               0       0
## Bare.nuclei               1           0               0       0
## Bl.cromatin               0           1               0       0
## Normal.nucleoli           0           0               1       0
## Mitoses                   0           0               0       1
## attr(,"term.labels")
## [1] "Cl.thickness"    "Cell.size"       "Cell.shape"      "Marg.adhesion"  
## [5] "Epith.c.size"    "Bare.nuclei"     "Bl.cromatin"     "Normal.nucleoli"
## [9] "Mitoses"        
## attr(,"order")
## [1] 1 1 1 1 1 1 1 1 1
## attr(,"intercept")
## [1] 1
## attr(,"response")
## [1] 1
## attr(,".Environment")
## <environment: R_GlobalEnv>
## attr(,"predvars")
## list(Class, Cl.thickness, Cell.size, Cell.shape, Marg.adhesion, 
##     Epith.c.size, Bare.nuclei, Bl.cromatin, Normal.nucleoli, 
##     Mitoses)
## attr(,"dataClasses")
##           Class    Cl.thickness       Cell.size      Cell.shape   Marg.adhesion 
##        "factor"       "ordered"       "ordered"       "ordered"       "ordered" 
##    Epith.c.size     Bare.nuclei     Bl.cromatin Normal.nucleoli         Mitoses 
##       "ordered"        "factor"        "factor"        "factor"        "factor" 
## 
## $call
## boosting(formula = Class ~ ., data = bc.train, boos = FALSE, 
##     mfinal = 200, coeflearn = "Freund", control = rpart.control(maxdepth = 1, 
##         minsplit = 10, cp = 0))
## 
## attr(,"vardep.summary")
##    benign malignant 
##       311       168 
## attr(,"class")
## [1] "boosting"

Langkah selanjutnya adalah membangun model Adaboost. Model AdaBoost dengan 50 learner dibentuk dari 479 data latih, terdiri atas 311 benign (64,9%) dan 168 malignant (35,1%). Setiap learner berupa decision stump dengan satu pemisahan. Pada learner pertama, Cell.size memisahkan sel berukuran 1–2 yang diprediksi benign dan ukuran 3–10 yang diprediksi malignant. Learner ini memiliki bobot terbesar, yaitu 2,57, dengan kesalahan klasifikasi terbobot sekitar 7%. Bobot learner menurun dari 2,57 menjadi 0,18 hingga iterasi ke-50. Hal ini menunjukkan bahwa learner awal memberikan pengaruh lebih besar, sedangkan learner berikutnya membantu menangani observasi yang sulit diklasifikasikan. Berdasarkan variable importance, Cell.size (41,86%) dan Bare.nuclei (30,64%) menjadi variabel paling berpengaruh dengan kontribusi gabungan sekitar 72,5%, diikuti Cl.thickness (8,95%) dan Marg.adhesion (7,33%). Sementara itu, Cell.shape memiliki kontribusi terendah (0,81%). Dengan demikian, ukuran sel dan inti sel merupakan variabel yang paling berperan dalam membedakan tumor jinak dan ganas.

Selanjutnya, dibentuk model AdaBoost dengan 100 learner. Model AdaBoost dengan 100 learner dibentuk dari 479 data latih, terdiri atas 311 benign dan 168 malignant, menggunakan decision stump sebagai weak learner. Learner pertama memiliki bobot terbesar, yaitu 2,57, dengan pemisah Cell.size, sedangkan bobot menurun hingga 0,14 pada learner ke-100. Hal ini menunjukkan bahwa learner awal lebih berpengaruh terhadap prediksi akhir, sementara learner berikutnya membantu memperbaiki klasifikasi. Berdasarkan variable importance, Cell.size (39,43%) dan Bare.nuclei (29,38%) menjadi variabel paling berpengaruh dengan kontribusi gabungan 68,81%, diikuti Cl.thickness (9,22%) dan Marg.adhesion (7,40%). Dibandingkan model 50 learner, urutan variabel penting tetap sama, tetapi kontribusi Cell.size dan Bare.nuclei sedikit menurun karena variabel lain, seperti Cell.shape dan Epith.c.size, mulai lebih banyak digunakan.

Model AdaBoost dengan 200 learner dibentuk dari 479 data latih, terdiri atas 311 benign dan 168 malignant, menggunakan decision stump sebagai weak learner. Learner pertama memiliki bobot terbesar, yaitu 2,57, dengan pemisah Cell.size, sedangkan bobot learner ke-200 turun menjadi 0,08. Hal ini menunjukkan bahwa learner awal lebih berpengaruh, sementara learner berikutnya hanya memberikan perbaikan kecil. Berdasarkan variable importance, Cell.size (37,36%) dan Bare.nuclei (28,17%) tetap menjadi variabel paling berpengaruh dengan kontribusi gabungan 65,53%, diikuti Cl.thickness (9,84%), Marg.adhesion (7,32%), dan Normal.nucleoli (5,93%). Dibandingkan model 50 dan 100 learner, urutan lima variabel teratas tetap sama, tetapi kontribusi Cell.size dan Bare.nuclei terus menurun karena variabel lain semakin banyak digunakan pada iterasi berikutnya.

4.5 Prediksi data testing

# Prediksi data testing
pred.ada50.obj  <- predict(boost.ada50,  newdata = bc.test)
pred.ada100.obj <- predict(boost.ada100, newdata = bc.test)
pred.ada200.obj <- predict(boost.ada200, newdata = bc.test)

# Prediksi kelas
pred.ada50  <- pred.ada50.obj$class
pred.ada100 <- pred.ada100.obj$class
pred.ada200 <- pred.ada200.obj$class

# Memastikan level factor sama dengan data aktual
pred.ada50  <- factor(pred.ada50,  levels = levels(bc.test$Class))
pred.ada100 <- factor(pred.ada100, levels = levels(bc.test$Class))
pred.ada200 <- factor(pred.ada200, levels = levels(bc.test$Class))

# Membandingkan aktual dan prediksi
hasil.ada <- data.frame(Aktual = bc.test$Class,
                        Pred_50 = pred.ada50,
                        Pred_100 = pred.ada100,
                        Pred_200 = pred.ada200)
head(hasil.ada, 10)

##       Aktual   Pred_50  Pred_100  Pred_200
## 1     benign malignant malignant malignant
## 2     benign    benign    benign    benign
## 3     benign    benign    benign    benign
## 4     benign    benign    benign    benign
## 5     benign    benign    benign    benign
## 6  malignant malignant malignant malignant
## 7     benign    benign    benign    benign
## 8     benign    benign    benign    benign
## 9     benign    benign    benign    benign
## 10 malignant malignant malignant malignant

Prediksi dilakukan pada 204 data testing menggunakan model AdaBoost dengan 50, 100, dan 200 learner. Hasil 10 observasi pertama menunjukkan bahwa ketiga model menghasilkan prediksi yang sama pada setiap observasi. Sebagian besar data benign diprediksi dengan benar sebagai benign, sedangkan data malignant pada observasi ke-6 dan ke-10 juga berhasil diprediksi dengan benar. Namun, observasi pertama yang aktualnya benign diprediksi sebagai malignant oleh ketiga model. Hasil ini merupakan gambaran awal, sehingga kinerja keseluruhan model perlu dinilai menggunakan confusion matrix dan metrik evaluasi lainnya.

4.6 Hasil Perbandingan

# Confusion Matrix
cm.ada50  <- confusionMatrix(data = pred.ada50,  reference = bc.test$Class, positive = "malignant")
cm.ada100 <- confusionMatrix(data = pred.ada100, reference = bc.test$Class, positive = "malignant")
cm.ada200 <- confusionMatrix(data = pred.ada200, reference = bc.test$Class, positive = "malignant")

cm.ada50

## Confusion Matrix and Statistics
## 
##            Reference
## Prediction  benign malignant
##   benign       130         4
##   malignant      3        67
##                                           
##                Accuracy : 0.9657          
##                  95% CI : (0.9306, 0.9861)
##     No Information Rate : 0.652           
##     P-Value [Acc > NIR] : <2e-16          
##                                           
##                   Kappa : 0.9241          
##                                           
##  Mcnemar's Test P-Value : 1               
##                                           
##             Sensitivity : 0.9437          
##             Specificity : 0.9774          
##          Pos Pred Value : 0.9571          
##          Neg Pred Value : 0.9701          
##              Prevalence : 0.3480          
##          Detection Rate : 0.3284          
##    Detection Prevalence : 0.3431          
##       Balanced Accuracy : 0.9606          
##                                           
##        'Positive' Class : malignant       
## 

cm.ada100

## Confusion Matrix and Statistics
## 
##            Reference
## Prediction  benign malignant
##   benign       130         4
##   malignant      3        67
##                                           
##                Accuracy : 0.9657          
##                  95% CI : (0.9306, 0.9861)
##     No Information Rate : 0.652           
##     P-Value [Acc > NIR] : <2e-16          
##                                           
##                   Kappa : 0.9241          
##                                           
##  Mcnemar's Test P-Value : 1               
##                                           
##             Sensitivity : 0.9437          
##             Specificity : 0.9774          
##          Pos Pred Value : 0.9571          
##          Neg Pred Value : 0.9701          
##              Prevalence : 0.3480          
##          Detection Rate : 0.3284          
##    Detection Prevalence : 0.3431          
##       Balanced Accuracy : 0.9606          
##                                           
##        'Positive' Class : malignant       
## 

cm.ada200

## Confusion Matrix and Statistics
## 
##            Reference
## Prediction  benign malignant
##   benign       130         5
##   malignant      3        66
##                                           
##                Accuracy : 0.9608          
##                  95% CI : (0.9242, 0.9829)
##     No Information Rate : 0.652           
##     P-Value [Acc > NIR] : <2e-16          
##                                           
##                   Kappa : 0.913           
##                                           
##  Mcnemar's Test P-Value : 0.7237          
##                                           
##             Sensitivity : 0.9296          
##             Specificity : 0.9774          
##          Pos Pred Value : 0.9565          
##          Neg Pred Value : 0.9630          
##              Prevalence : 0.3480          
##          Detection Rate : 0.3235          
##    Detection Prevalence : 0.3382          
##       Balanced Accuracy : 0.9535          
##                                           
##        'Positive' Class : malignant       
## 

Hasil evaluasi menunjukkan bahwa model AdaBoost dengan 50 dan 100 learner memperoleh kinerja yang sama, dengan accuracy sebesar 96,57%, sensitivity 94,37%, dan specificity 97,74%. Kedua model berhasil mengklasifikasikan 130 data benign dan 67 data malignant dengan benar, serta menghasilkan 7 kesalahan klasifikasi. Sementara itu, model dengan 200 learner memperoleh accuracy 96,08% dan sensitivity 92,96%, dengan 196 prediksi benar dan 8 kesalahan klasifikasi.Secara keseluruhan, model 50 dan 100 learner menunjukkan kinerja terbaik karena memiliki accuracy, sensitivity, dan specificity yang lebih tinggi dibandingkan model 200 learner. Nilai Kappa masing-masing sebesar 0,9241 dan 0,913 menunjukkan kesesuaian prediksi yang sangat baik. Dengan demikian, penambahan jumlah learner menjadi 200 tidak meningkatkan kinerja model pada data testing.

4.7 Menghitung Ukuran Evaluasi Klasifikasi

# Menghitung ukuran evaluasi klasifikasi
tab.ada50  <- table(Aktual = bc.test$Class, Prediksi = pred.ada50)
tab.ada100 <- table(Aktual = bc.test$Class, Prediksi = pred.ada100)
tab.ada200 <- table(Aktual = bc.test$Class, Prediksi = pred.ada200)

tab.ada50

##            Prediksi
## Aktual      benign malignant
##   benign       130         3
##   malignant      4        67

tab.ada100

##            Prediksi
## Aktual      benign malignant
##   benign       130         3
##   malignant      4        67

tab.ada200

##            Prediksi
## Aktual      benign malignant
##   benign       130         3
##   malignant      5        66

# Fungsi untuk menghitung ukuran evaluasi (kelas positif = malignant)
evaluasi <- function(tab) {
  TN <- tab["benign", "benign"]
  FP <- tab["benign", "malignant"]
  FN <- tab["malignant", "benign"]
  TP <- tab["malignant", "malignant"]
  
  Accuracy    <- (TP + TN) / (TP + TN + FP + FN)
  Precision   <- TP / (TP + FP)
  Recall      <- TP / (TP + FN)
  Specificity <- TN / (TN + FP)
  F1          <- 2 * (Precision * Recall) / (Precision + Recall)
  
  c(Accuracy = Accuracy, Precision = Precision, Recall = Recall,
    Specificity = Specificity, F1_Score = F1)
}

evaluasi.ada <- data.frame(
  Model = c("AdaBoost 50 learner", "AdaBoost 100 learner", "AdaBoost 200 learner"),
  rbind(evaluasi(tab.ada50), evaluasi(tab.ada100), evaluasi(tab.ada200))
)

# Tabel perbandingan performa
evaluasi.ada[, -1] <- round(evaluasi.ada[, -1], 4)
evaluasi.ada

##                  Model Accuracy Precision Recall Specificity F1_Score
## 1  AdaBoost 50 learner   0.9657    0.9571 0.9437      0.9774   0.9504
## 2 AdaBoost 100 learner   0.9657    0.9571 0.9437      0.9774   0.9504
## 3 AdaBoost 200 learner   0.9608    0.9565 0.9296      0.9774   0.9429

Berdasarkan hasil evaluasi, model AdaBoost dengan 50 dan 100 learner memperoleh kinerja yang sama, yaitu accuracy 96,57%, precision 95,71%, recall 94,37%, specificity 97,74%, dan F1-score 95,04%. Sementara itu, model dengan 200 learner memiliki nilai accuracy 96,08% dan F1-score 94,29%, dengan penurunan recall menjadi 92,96%, sedangkan specificity tetap 97,74%. Dengan demikian, model AdaBoost 50 dan 100 learner menunjukkan performa terbaik karena menghasilkan nilai evaluasi tertinggi dan sama. Model 50 learner dapat dipilih karena memberikan kinerja yang setara dengan model 100 learner menggunakan jumlah learner yang lebih sedikit. Penambahan jumlah learner menjadi 200 tidak meningkatkan performa klasifikasi pada data testing.

4.8 Perkembangan Error Adaboost

# Perkembangan Error AdaBoost
# Menghitung error training dan testing
error.train50  <- errorevol(boost.ada50,  newdata = bc.train)
error.test50   <- errorevol(boost.ada50,  newdata = bc.test)

error.train100 <- errorevol(boost.ada100, newdata = bc.train)
error.test100  <- errorevol(boost.ada100, newdata = bc.test)

error.train200 <- errorevol(boost.ada200, newdata = bc.train)
error.test200  <- errorevol(boost.ada200, newdata = bc.test)

# Melihat nilai error
head(error.train50$error)

## [1] 0.07098121 0.07098121 0.05636743 0.05219207 0.05219207 0.04592902

head(error.test50$error)

## [1] 0.07843137 0.07843137 0.05882353 0.05392157 0.05392157 0.05882353

# Membandingkan error training dan testing (3 plot dalam satu tampilan)
# Skala sumbu-y dibuat sama agar ketiga plot mudah dibandingkan
ymax <- max(error.train50$error,  error.test50$error,
            error.train100$error, error.test100$error,
            error.train200$error, error.test200$error)

plot.error <- function(err.train, err.test, judul) {
  plot(err.test$error, type = "l", col = "red", lwd = 2,
       ylim = c(0, ymax), main = judul,
       xlab = "Jumlah Learner", ylab = "Error")
  lines(err.train$error, col = "blue", lwd = 2, lty = 2)
  legend("topright", legend = c("Testing", "Training"),
         col = c("red", "blue"), lty = c(1, 2), lwd = 2, bty = "n")
}

op <- par(mfrow = c(1, 3))
plot.error(error.train50,  error.test50,  "AdaBoost - 50 Learner")
plot.error(error.train100, error.test100, "AdaBoost - 100 Learner")
plot.error(error.train200, error.test200, "AdaBoost - 200 Learner")

par(op)

Pada seluruh model, error data latih menurun seiring bertambahnya jumlah learner, dari 7,10% pada iterasi pertama menjadi sekitar 2,3%, 1,5%, dan 1,3% pada 50, 100, dan 200 learner. Sementara itu, error data uji turun hingga sekitar 3,4% pada iterasi ke-25, kemudian cenderung stabil dan sedikit meningkat menjadi 3,9% pada model 200 learner. Hal ini menunjukkan bahwa penambahan learner setelah iterasi ke-25 tidak lagi memperbaiki prediksi pada data uji dan mulai menunjukkan kecenderungan overfitting. Dengan demikian, model 50 dan 100 learner memberikan kinerja data uji yang sama baik, sedangkan model 200 learner tidak memberikan peningkatan performa.

4.9 Variable Importance

# Variable Importance
importance.ada50  <- sort(boost.ada50$importance,  decreasing = TRUE)
importance.ada100 <- sort(boost.ada100$importance, decreasing = TRUE)
importance.ada200 <- sort(boost.ada200$importance, decreasing = TRUE)

importance.ada50

##       Cell.size     Bare.nuclei    Cl.thickness   Marg.adhesion Normal.nucleoli 
##      41.8577541      30.6417743       8.9543959       7.3254615       4.7379328 
##         Mitoses     Bl.cromatin    Epith.c.size      Cell.shape 
##       2.5675302       1.7409291       1.3692136       0.8050084

importance.ada100

##       Cell.size     Bare.nuclei    Cl.thickness   Marg.adhesion Normal.nucleoli 
##       39.432729       29.377745        9.222131        7.403588        5.515949 
##         Mitoses     Bl.cromatin      Cell.shape    Epith.c.size 
##        2.924061        2.153168        2.063869        1.906759

importance.ada200

##       Cell.size     Bare.nuclei    Cl.thickness   Marg.adhesion Normal.nucleoli 
##       37.360946       28.171016        9.835175        7.317661        5.932666 
##         Mitoses    Epith.c.size      Cell.shape     Bl.cromatin 
##        3.170586        2.965481        2.740277        2.506192

# Plot variable importance
par(mfrow = c(1, 3))
barplot(importance.ada50,  las = 2, main = "AdaBoost - 50 Learner",  ylab = "Importance")
barplot(importance.ada100, las = 2, main = "AdaBoost - 100 Learner", ylab = "Importance")
barplot(importance.ada200, las = 2, main = "AdaBoost - 200 Learner", ylab = "Importance")

par(mfrow = c(1, 1))

Pada ketiga model,Cell.size dan Bare.nuclei menjadi variabel paling berpengaruh, diikuti Cl.thickness, Marg.adhesion, dan Normal.nucleoli. Sementara itu, Mitoses, Bl.cromatin, Epith.c.size, dan Cell.shape memberikan kontribusi lebih kecil. Seiring bertambahnya jumlah learner, kontribusi dua variabel utama cenderung menurun karena variabel lain mulai digunakan untuk memperbaiki klasifikasi observasi yang sulit diprediksi.

BAB V KESIMPULAN

5.1 Kesimpulan

Metode Boosting merupakan salah satu metode ensemble learning yang menggabungkan beberapa weak learner secara bertahap untuk menghasilkan model dengan prediksi yang lebih baik. Setiap learner baru berfokus memperbaiki kesalahan dari learner sebelumnya.

Implementasi Boosting untuk kasus klasifikasi dan regresi dapat dilakukan menggunakan R dengan menyesuaikan algoritma dan variabel respons yang digunakan. Pada klasifikasi, model digunakan untuk memprediksi kategori, sedangkan pada regresi digunakan untuk memprediksi nilai numerik.

Evaluasi performa model dilakukan menggunakan ukuran yang sesuai dengan jenis analisis. Pada klasifikasi, evaluasi dapat menggunakan accuracy, precision, recall, specificity, dan F1-score, sedangkan pada regresi dapat menggunakan RMSE, MAE, dan \(R^2\). Hasil evaluasi tersebut digunakan untuk menilai kemampuan model dalam menghasilkan prediksi yang baik.

Berdasarkan penerapan metode AdaBoost pada data BreastCancer menggunakan R, model dengan 50 dan 100 learner memberikan performa terbaik dengan accuracy 96,57% dan F1-score 95,04%. Keduanya mampu mengklasifikasikan tumor jinak (benign) dan ganas (malignant) dengan hasil yang sama, sedangkan model 200 learner mengalami sedikit penurunan performa. Variabel yang paling berpengaruh dalam klasifikasi adalah Cell.size dan Bare.nuclei. Selain itu, error data uji cenderung stabil setelah sekitar 25 iterasi, sehingga penambahan jumlah learner tidak selalu meningkatkan performa model.

DAFTAR PUSTAKA

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