BAB I PENDAHULUAN

1.1 Latar Belakang Masalah

Perkembangan teknologi informasi mendorong pemanfaatan machine learning dalam mengolah data dan menghasilkan prediksi yang dapat mendukung pengambilan keputusan. Salah satu pendekatan dalam machine learning adalah ensemble learning, yaitu metode yang menggabungkan beberapa model pembelajaran untuk menghasilkan prediksi yang lebih baik. Salah satu metode ensemble learning yang banyak digunakan adalah boosting. Metode ini membangun model secara bertahap dengan memperhatikan kesalahan prediksi dari model sebelumnya sehingga model berikutnya dapat memperbaiki kesalahan tersebut. Metode seperti AdaBoost, Gradient Boosting, dan XGBoost dapat digunakan untuk meningkatkan kemampuan prediksi dalam permasalahan klasifikasi (Wibisono dkk, 2024).

Metode boosting dapat diterapkan pada berbagai permasalahan analisis data, baik klasifikasi maupun regresi. Pada klasifikasi, metode ini digunakan untuk memprediksi data ke dalam kategori tertentu, sedangkan pada regresi digunakan untuk memperkirakan nilai numerik suatu variabel. Kinerja metode boosting perlu dievaluasi menggunakan ukuran yang sesuai dengan jenis permasalahannya agar kemampuan model dalam menghasilkan prediksi dapat diketahui. Penerapan teknik boosting, seperti AdaBoost dan XGBoost, dapat menghasilkan kinerja klasifikasi yang baik berdasarkan ukuran akurasi, presisi, recall, dan F1-score (Dinata dkk, 2023).

pemahaman mengenai metode boosting penting untuk mengembangkan keterampilan dalam pemodelan prediktif. Melalui praktikum ini, mahasiswa diharapkan dapat memahami konsep dasar boosting, mengimplementasikannya untuk kasus klasifikasi dan regresi menggunakan R, serta mengevaluasi performa model berdasarkan ukuran evaluasi yang sesuai. Dengan demikian, mahasiswa dapat memahami proses pembentukan model boosting sekaligus menilai kemampuan model dalam menghasilkan prediksi.

1.2 Rumusan Masalah

Berdasarkan latar belakang, rumusan masalah yang dapat disimpulkan adalah:

  1. Apa yang dimaksud dengan Boosting sebagai metode ensemble learning?

  2. Bagaimana cara mengimplementasikan Boosting untuk kasus klasifikasi dan regresi menggunakan R?

  3. Bagaimana cara mengevaluasi perfoma model menggunakan ukuran evaluasi yang sesuai?

1.3 Tujuan Penelitian

Berdasarkan rumusan masalah, tujuan yang dapat disimpulkan adalah:

  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 perfoma model menggunakan ukuran evaluasi yang sesuai.

1.4 Manfaat Penelitian

Adapun manfaat pada penelitian ini, yaitu:

  1. Bagi penulis
    1. Mengembangkan dan menerapkan wawasan ilmu yang diperoleh dalam bidang statistika khususnya tentang Boosting
    2. Dapat memberikan informasi tentang Boosting
  2. Bagi pembaca
    1. Menambah ilmu pengetahuan dan pemahaman yang berkaitan tentang Boosting
    2. Menjadi rujukan bagi peneliti selanjutnya

1.5 Batasan Masalah

Adapun batasan masalah pada laporan praktikum pertemuan ini adalah lakukan analisis Boosting secara lengkap mulai dari persiapan data, pembentukan model, prediksi, evaluasi, visualisasi, dan interpretasi. Gunakan set.seed(234) agar hasil pembagian data dapat direproduksi. Lakukan klasifikasi menggunakan AdaBoost pada data BreastCancer. Clue: Gunakan adabag::boosting() dengan decision stump sebagai weak learner. Bandingkan untuk 50, 100 dan 200 jumlah learner. Bandingkan pula performa model berdasarkan confusion matrix, Accuracy, Precision, Recall, Specificity, dan F1-score.

1.6 Sistematika Penulisan

Adapun sistematika penulisan dari praktikum ini adalah:

BAB I PENDAHULUAN Bab I terdiri dari latar belakang, rumusan masalah, batasan masalah, tujuan penelitian, manfaat penelitian, dan sistematika penulisan.

BAB II TINJAUAN PUSTAKA Bab tinjauan pustaka ini meliputi landasan teori yang berisi tentang pembahasan teori yang digunakan dalam penelitian.

BAB III METODE PENELITIAN Bab ini terdiri dari analisis jenis dan sumber data, variabel penelitian, algoritma penelitian, dan diagram alur.

BAB IV HASIL DAN PEMBAHASAN Bab ini merupakan tahapan pencapaian hasil yang diperoleh dari analisis data yang dilakukan serta penjelasan dari hasil tersebut di bagian pembahasan.

BAB V KESIMPULAN DAN SARAN Bab ini merupakan bagian penutup dalam laporan ini berupa penarikan kesimpulan mengenai hasil analisis dari penelitian serta penyampaian saran oleh penulis.

DAFTAR PUSTAKA

LAMPIRAN

BAB II TINJAUAN PUSTAKA

2.1 Boosting

Boosting merupakan salah satu metode ensemble learning yang menggabungkan beberapa model pembelajaran sederhana (weak learners) secara bertahap untuk menghasilkan model prediksi yang lebih kuat. Prinsip utama metode ini adalah memperbaiki kesalahan prediksi dari model sebelumnya melalui pembentukan model berikutnya. Dengan demikian, setiap tahapan pembelajaran berperan dalam meningkatkan kemampuan model secara keseluruhan. Berbeda dengan bagging yang membangun beberapa model secara paralel, boosting membangun model secara berurutan sehingga proses pembelajaran berikutnya bergantung pada hasil tahap sebelumnya (Sihombing, 2025).

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 (Fransiska, 2026).

Metode boosting dapat digunakan untuk menyelesaikan permasalahan klasifikasi maupun regresi. Pada klasifikasi, metode ini digunakan untuk memprediksi kelas atau kategori suatu pengamatan, sedangkan pada regresi digunakan untuk memperkirakan nilai numerik dari variabel respons. Beberapa algoritma boosting yang umum digunakan adalah Adaptive Boosting (AdaBoost), Gradient Boosting, dan Extreme Gradient Boosting (XGBoost). Setiap algoritma memiliki mekanisme pembelajaran yang berbeda, tetapi memiliki tujuan yang sama, yaitu meningkatkan kemampuan prediksi melalui penggabungan model (Amatullah dkk, 2025).

2.2 Adaptive Boosting (AdaBoost)

Adaptive Boosting atau AdaBoost merupakan salah satu algoritma boosting yang bekerja dengan memberikan bobot berbeda pada setiap pengamatan dalam data. Pada awal pembelajaran, setiap pengamatan diberikan bobot yang sama. Setelah model pertama dibentuk, bobot pengamatan yang salah diklasifikasikan akan ditingkatkan, sedangkan bobot pengamatan yang diklasifikasikan dengan benar akan disesuaikan. Proses ini membuat model berikutnya lebih memperhatikan pengamatan yang sulit diprediksi. Tahapan tersebut dilakukan secara berulang hingga diperoleh gabungan model yang digunakan untuk menghasilkan prediksi akhir (Sukmawati dkk, 2024).

Pada kasus klasifikasi, AdaBoost menggabungkan hasil prediksi dari beberapa model dasar dengan mempertimbangkan tingkat kesalahan masing-masing model. Model dasar yang memiliki kinerja lebih baik dapat memberikan kontribusi lebih besar dalam menentukan prediksi akhir. Sementara itu, penerapan AdaBoost untuk regresi dapat dilakukan menggunakan pengembangan algoritma yang dirancang untuk memprediksi nilai numerik. Dengan demikian, penerapan AdaBoost perlu disesuaikan dengan jenis permasalahan yang dianalisis.

BAB III METODE PENELITIAN

3.1 Jenis dan Sumber Data

Jenis data yang digunakan di dalam penelitian ini berupa data kuantitatif. Data kuantitatif merupakan data atau informasi yang didapatkan dalam bentuk angka atau numerik. Data yang digunakan merupakan data sekunder dataset BreastCancer yang tersedia dalam paket mlbench pada program RStudio. Dataset ini berisi karakteristik sel tumor payudara yang digunakan untuk mengklasifikasikan kondisi tumor ke dalam dua kategori, yaitu benign (jinak) dan malignant (ganas). Dalam analisis ini, metode AdaBoost Classification diterapkan dengan membandingkan jumlah learner sebanyak 50, 100, dan 200 untuk mengetahui perbedaan kinerja klasifikasi berdasarkan jumlah model dasar yang digunakan.

3.2 Variabel Penelitian

Variabel penelitian adalah karakter atau segala sesuatu yang menjadi perhatian dalam suatu penelitian. Pada penelitian ini terdapat 10 variabel prediktor dan 1 variabel respon. Variabel prediktor meliputi Cl.thickness (ketebalan gumpalan sel), Cell.size (keseragaman ukuran sel), Cell.shape (keseragaman bentuk sel), Marg.adhesion (adhesi marginal), Epith.c.size (ukuran sel epitel), Bare.nuclei (inti sel yang tidak tertutup), Bl.cromatin (kromatin lunak), Normal.nucleoli (nukleolus normal), dan Mitoses (aktivitas mitosis). Sementara itu, variabel respon adalah Class, yang menunjukkan kategori kondisi tumor payudara, yaitu benign (jinak) dan malignant (ganas).

3.3 Langkah-langkah Analisis

Berikut adalah algoritma untuk menyelesaiankan batasan masalah:

  1. Memanggil package yang diperlukan dan memuat dataset BreastCancer.

  2. Memeriksa struktur, ringkasan data.

  3. Menyiapkan variabel Class sebagai variabel respon.

  4. Membagi data menjadi 70% data training dan 30% data testing.

  5. Membangun model AdaBoost dengan jumlah learner sebanyak 50, 100, dan 200.

  6. Melakukan prediksi terhadap data testing.

  7. Membentuk confusion matrix untuk membandingkan kelas aktual dengan kelas hasil prediksi pada setiap model.

  8. Menghitung metrik evaluasi yang meliputi accuracy, precision, recall, specificity, dan F1-score.

  9. Membandingkan nilai metrik evaluasi ketiga model.

  10. Menghitung dan membandingkan training error serta testing error.

  11. Menganalisis variable importance untuk mengetahui variabel prediktor yang memiliki kontribusi terbesar dalam proses klasifikasi.

  12. Menentukan model terbaik berdasarkan hasil evaluasi.

BAB IV HASIL DAN PEMBAHASAN

4.1 Library dan Statistik Deskriptif

# A1. MEMANGGIL LIBRARY DAN MEMUAT DATA
library(adabag)
## Loading required package: rpart
## Loading required package: caret
## Loading required package: ggplot2
## Loading required package: lattice
## Loading required package: foreach
## Loading required package: doParallel
## Loading required package: iterators
## Loading required package: parallel
library(mlbench)
library(caret)
library(rpart)

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

# 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  
##                 
##                 
##                 
##                 
##

Dataset diimpor menggunakan perintah data(BreastCancer, package = “mlbench”). Berdasarkan hasil str(bc), dataset awal terdiri atas 699 observasi dan 11 variabel, yaitu Id, sembilan variabel prediktor, dan Class sebagai variabel respons. Variabel prediktor meliputi Cl.thickness, Cell.size, Cell.shape, Marg.adhesion, Epith.c.size, Bare.nuclei, Bl.cromatin, Normal.nucleoli, dan Mitoses. Variabel Class memiliki dua kategori, yaitu benign dan malignant.

Berdasarkan hasil statistik deskriptif, menunjukkan bahwa terdapat 458 observasi berkategori benign dan 241 observasi berkategori malignant. Dengan demikian, kelas benign memiliki jumlah observasi lebih banyak dibandingkan kelas malignant. Ringkasan ini memberikan gambaran awal mengenai struktur data dan distribusi kelas yang digunakan dalam proses klasifikasi AdaBoost.

4.2 Persiapan Data

# A2. PERSIAPAN DATA
# Menghapus variabel identifier
bc$Id <- NULL

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

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

# 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

Berdasarkan hasil, data yang digunakan pada tahap selanjutnya terdiri atas 444 observasi benign dan 239 observasi malignant. Proporsi masing-masing kelas adalah sekitar 65,01% untuk benign dan 34,99% untuk malignant. Hasil tersebut menunjukkan bahwa distribusi kelas belum seimbang karena jumlah observasi benign lebih besar daripada malignant. Informasi distribusi kelas ini penting untuk membantu memahami hasil evaluasi model pada tahap berikutnya.

4.3 Membagi Data Training dan Testing

# A3. MEMBAGI DATA TRAINING DAN TESTING (70:30)
set.seed(234)

idx.cls <- createDataPartition(
  bc$Class,
  p = 0.70,
  list = FALSE
)

bc.train <- bc[idx.cls, ]
bc.test  <- bc[-idx.cls, ]

# Ukuran data
dim(bc.train)
## [1] 479  10
dim(bc.test)
## [1] 204  10
# Distribusi kelas training
table(bc.train$Class)
## 
##    benign malignant 
##       311       168
prop.table(table(bc.train$Class))
## 
##    benign malignant 
## 0.6492693 0.3507307
# Distribusi kelas testing
table(bc.test$Class)
## 
##    benign malignant 
##       133        71
prop.table(table(bc.test$Class))
## 
##    benign malignant 
## 0.6519608 0.3480392

Berdasarkan hasil, data training terdiri atas 479 observasi dan 10 variabel, sedangkan data testing terdiri atas 204 observasi dan 10 variabel. Pada data training, terdapat 311 observasi benign dan 168 observasi malignant. Sementara itu, data testing terdiri atas 133 observasi benign dan 71 observasi malignant. Proporsi kelas pada data training adalah sekitar 64,93% benign dan 35,07% malignant, sedangkan pada data testing sekitar 65,20% benign dan 34,80% malignant. Proporsi yang relatif serupa menunjukkan bahwa pembagian data tetap mempertahankan komposisi kedua kelas. Data training selanjutnya digunakan untuk membentuk model AdaBoost, sedangkan data testing digunakan untuk mengevaluasi kemampuan model dalam mengklasifikasikan observasi yang tidak digunakan selama pelatihan.

4.4 Pembentukan Model AdaBoost

# A4. MEMBENTUK MODEL ADABOOST
# Jumlah learner yang dibandingkan
jumlah.learner <- c(50, 100, 200)

# Menyimpan model
model.ada <- list()

for (m in jumlah.learner) {
  
  set.seed(234)
  
  model.ada[[as.character(m)]] <- boosting(
    Class ~ .,
    data = bc.train,
    boos = FALSE,
    mfinal = m,
    coeflearn = "Freund",
    control = rpart.control(
      maxdepth = 1,
      minsplit = 10,
      cp = 0
    )
  )
}

# Melihat ringkasan model
model.ada[["50"]]
## $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
##  [96,] 0.71611943 0.2838806
##  [97,] 0.50715776 0.4928422
##  [98,] 0.28691934 0.7130807
##  [99,] 0.67835174 0.3216483
## [100,] 0.39927377 0.6007262
## [101,] 0.67728707 0.3227129
## [102,] 0.75252833 0.2474717
## [103,] 0.31780742 0.6821926
## [104,] 0.71611943 0.2838806
## [105,] 0.71611943 0.2838806
## [106,] 0.14796084 0.8520392
## [107,] 0.27861189 0.7213881
## [108,] 0.74315857 0.2568414
## [109,] 0.71611943 0.2838806
## [110,] 0.62958365 0.3704164
## [111,] 0.70639956 0.2936004
## [112,] 0.18391123 0.8160888
## [113,] 0.71611943 0.2838806
## [114,] 0.71476064 0.2852394
## [115,] 0.75252833 0.2474717
## [116,] 0.71611943 0.2838806
## [117,] 0.71611943 0.2838806
## [118,] 0.28612313 0.7138769
## [119,] 0.71611943 0.2838806
## [120,] 0.36641947 0.6335805
## [121,] 0.67763718 0.3223628
## [122,] 0.67835174 0.3216483
## [123,] 0.75252833 0.2474717
## [124,] 0.31195649 0.6880435
## [125,] 0.28612313 0.7138769
## [126,] 0.19180014 0.8081999
## [127,] 0.69171921 0.3082808
## [128,] 0.67763718 0.3223628
## [129,] 0.71611943 0.2838806
## [130,] 0.71611943 0.2838806
## [131,] 0.47230461 0.5276954
## [132,] 0.63360171 0.3663983
## [133,] 0.75252833 0.2474717
## [134,] 0.71611943 0.2838806
## [135,] 0.22073742 0.7792626
## [136,] 0.71611943 0.2838806
## [137,] 0.71611943 0.2838806
## [138,] 0.14933533 0.8506647
## [139,] 0.31354150 0.6864585
## [140,] 0.63986949 0.3601305
## [141,] 0.11765104 0.8823490
## [142,] 0.71611943 0.2838806
## [143,] 0.05637943 0.9436206
## [144,] 0.09303638 0.9069636
## [145,] 0.33761221 0.6623878
## [146,] 0.71611943 0.2838806
## [147,] 0.28518556 0.7148144
## [148,] 0.71611943 0.2838806
## [149,] 0.54040953 0.4595905
## [150,] 0.41639695 0.5836031
## [151,] 0.19637882 0.8036212
## [152,] 0.71611943 0.2838806
## [153,] 0.18461779 0.8153822
## [154,] 0.49352214 0.5064779
## [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
## [182,] 0.40032514 0.5996749
## [183,] 0.78164083 0.2183592
## [184,] 0.71611943 0.2838806
## [185,] 0.71611943 0.2838806
## [186,] 0.71611943 0.2838806
## [187,] 0.32776766 0.6722323
## [188,] 0.71611943 0.2838806
## [189,] 0.26590114 0.7340989
## [190,] 0.27834349 0.7216565
## [191,] 0.29203323 0.7079668
## [192,] 0.20479443 0.7952056
## [193,] 0.04952872 0.9504713
## [194,] 0.19076194 0.8092381
## [195,] 0.71611943 0.2838806
## [196,] 0.24606906 0.7539309
## [197,] 0.27933279 0.7206672
## [198,] 0.51344540 0.4865546
## [199,] 0.69412282 0.3058772
## [200,] 0.42062613 0.5793739
## [201,] 0.36860474 0.6313953
## [202,] 0.71611943 0.2838806
## [203,] 0.04952872 0.9504713
## [204,] 0.71611943 0.2838806
## [205,] 0.26894689 0.7310531
## [206,] 0.14796084 0.8520392
## [207,] 0.71611943 0.2838806
## [208,] 0.64893391 0.3510661
## [209,] 0.71611943 0.2838806
## [210,] 0.75252833 0.2474717
## [211,] 0.42641973 0.5735803
## [212,] 0.67835174 0.3216483
## [213,] 0.38306042 0.6169396
## [214,] 0.33425151 0.6657485
## [215,] 0.71611943 0.2838806
## [216,] 0.46914154 0.5308585
## [217,] 0.30518181 0.6948182
## [218,] 0.71611943 0.2838806
## [219,] 0.26765262 0.7323474
## [220,] 0.71611943 0.2838806
## [221,] 0.73733696 0.2626630
## [222,] 0.39905934 0.6009407
## [223,] 0.37121359 0.6287864
## [224,] 0.22485153 0.7751485
## [225,] 0.67763718 0.3223628
## [226,] 0.74315857 0.2568414
## [227,] 0.33902495 0.6609750
## [228,] 0.30956433 0.6904357
## [229,] 0.75252833 0.2474717
## [230,] 0.71611943 0.2838806
## [231,] 0.67835174 0.3216483
## [232,] 0.31910169 0.6808983
## [233,] 0.24041429 0.7595857
## [234,] 0.71611943 0.2838806
## [235,] 0.75252833 0.2474717
## [236,] 0.18069965 0.8193004
## [237,] 0.73597817 0.2640218
## [238,] 0.51289349 0.4871065
## [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
## [245,] 0.17225159 0.8277484
## [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
## [253,] 0.14248462 0.8575154
## [254,] 0.17900091 0.8209991
## [255,] 0.65395152 0.3460485
## [256,] 0.72812811 0.2718719
## [257,] 0.67763718 0.3223628
## [258,] 0.67763718 0.3223628
## [259,] 0.71611943 0.2838806
## [260,] 0.75252833 0.2474717
## [261,] 0.71611943 0.2838806
## [262,] 0.54738397 0.4526160
## [263,] 0.75252833 0.2474717
## [264,] 0.22073742 0.7792626
## [265,] 0.75252833 0.2474717
## [266,] 0.66377876 0.3362212
## [267,] 0.29788416 0.7021158
## [268,] 0.54738397 0.4526160
## [269,] 0.78164083 0.2183592
## [270,] 0.70639956 0.2936004
## [271,] 0.71611943 0.2838806
## [272,] 0.19637882 0.8036212
## [273,] 0.71611943 0.2838806
## [274,] 0.71476064 0.2852394
## [275,] 0.69171921 0.3082808
## [276,] 0.71404608 0.2859539
## [277,] 0.78164083 0.2183592
## [278,] 0.22574679 0.7742532
## [279,] 0.75252833 0.2474717
## [280,] 0.77401426 0.2259857
## [281,] 0.71611943 0.2838806
## [282,] 0.67763718 0.3223628
## [283,] 0.57350743 0.4264926
## [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
## [296,] 0.71611943 0.2838806
## [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
## [303,] 0.45730533 0.5426947
## [304,] 0.71404608 0.2859539
## [305,] 0.71404608 0.2859539
## [306,] 0.58366282 0.4163372
## [307,] 0.67173386 0.3282661
## [308,] 0.71576932 0.2842307
## [309,] 0.63360171 0.3663983
## [310,] 0.75252833 0.2474717
## [311,] 0.75252833 0.2474717
## [312,] 0.71404608 0.2859539
## [313,] 0.71476064 0.2852394
## [314,] 0.22741889 0.7725811
## [315,] 0.59583402 0.4041660
## [316,] 0.70849287 0.2915071
## [317,] 0.13446299 0.8655370
## [318,] 0.69710384 0.3028962
## [319,] 0.67001061 0.3299894
## [320,] 0.77956747 0.2204325
## [321,] 0.71404608 0.2859539
## [322,] 0.32264736 0.6773526
## [323,] 0.71404608 0.2859539
## [324,] 0.71611943 0.2838806
## [325,] 0.67627839 0.3237216
## [326,] 0.71404608 0.2859539
## [327,] 0.71404608 0.2859539
## [328,] 0.75252833 0.2474717
## [329,] 0.71404608 0.2859539
## [330,] 0.71404608 0.2859539
## [331,] 0.26765262 0.7323474
## [332,] 0.67627839 0.3237216
## [333,] 0.16788428 0.8321157
## [334,] 0.25765043 0.7423496
## [335,] 0.63396616 0.3660338
## [336,] 0.67835174 0.3216483
## [337,] 0.07309111 0.9269089
## [338,] 0.46659711 0.5334029
## [339,] 0.48543640 0.5145636
## [340,] 0.75252833 0.2474717
## [341,] 0.31780742 0.6821926
## [342,] 0.63986949 0.3601305
## [343,] 0.21446499 0.7855350
## [344,] 0.52802242 0.4719776
## [345,] 0.67835174 0.3216483
## [346,] 0.67763718 0.3223628
## [347,] 0.67763718 0.3223628
## [348,] 0.67763718 0.3223628
## [349,] 0.67763718 0.3223628
## [350,] 0.67763718 0.3223628
## [351,] 0.75252833 0.2474717
## [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 = m, coeflearn = "Freund", control = rpart.control(maxdepth = 1, 
##         minsplit = 10, cp = 0))
## 
## attr(,"vardep.summary")
##    benign malignant 
##       311       168 
## attr(,"class")
## [1] "boosting"
model.ada[["100"]]
## $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
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## 
## $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 = m, coeflearn = "Freund", control = rpart.control(maxdepth = 1, 
##         minsplit = 10, cp = 0))
## 
## attr(,"vardep.summary")
##    benign malignant 
##       311       168 
## attr(,"class")
## [1] "boosting"
model.ada[["200"]]
## $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
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## [354,] 0.65100013 0.3489999
## [355,] 0.67101411 0.3289859
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## [359,] 0.68543885 0.3145612
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## [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
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## [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
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## [375,] 0.62039401 0.3796060
## [376,] 0.60458343 0.3954166
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## [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
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## [395,] 0.65483273 0.3451673
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## [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
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## [406,] 0.65100013 0.3489999
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## [409,] 0.64040799 0.3595920
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## [411,] 0.64040799 0.3595920
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## [418,] 0.65100013 0.3489999
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## [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 = m, coeflearn = "Freund", control = rpart.control(maxdepth = 1, 
##         minsplit = 10, cp = 0))
## 
## attr(,"vardep.summary")
##    benign malignant 
##       311       168 
## attr(,"class")
## [1] "boosting"

Pemodelan dilakukan dengan tiga variasi jumlah learner, yaitu 50, 100, dan 200. Metode AdaBoost membangun beberapa model dasar secara bertahap untuk meningkatkan kemampuan klasifikasi. Parameter maxdepth=1 digunakan agar setiap pohon keputusan memiliki kedalaman satu, sedangkan coeflearn=“Freund” menentukan metode pembobotan dalam proses pembelajaran. Ketiga model tersebut kemudian dibandingkan untuk mengetahui apakah penambahan jumlah learner dapat meningkatkan kinerja klasifikasi.

4.5 Prediksi dan Confusion Matrix

# A5. MELAKUKAN PREDIKSI DATA TESTING
pred.ada <- list()
hasil.ada <- list()

for (m in jumlah.learner) {
  
  nama <- as.character(m)
  
  # Prediksi model
  pred.obj <- predict(
    model.ada[[nama]],
    newdata = bc.test
  )
  
  # Mengambil prediksi kelas
  pred <- factor(
    pred.obj$class,
    levels = levels(bc.test$Class)
  )
  
  pred.ada[[nama]] <- pred
  
  # Membandingkan aktual dan prediksi
  hasil.ada[[nama]] <- data.frame(
    Aktual = bc.test$Class,
    Prediksi = pred
  )
  
  cat("\nHasil Prediksi -", m, "Learner\n")
  print(head(hasil.ada[[nama]], 10))
}
## 
## Hasil Prediksi - 50 Learner
##       Aktual  Prediksi
## 1     benign malignant
## 2     benign    benign
## 3     benign    benign
## 4     benign    benign
## 5     benign    benign
## 6  malignant malignant
## 7     benign    benign
## 8     benign    benign
## 9     benign    benign
## 10 malignant malignant
## 
## Hasil Prediksi - 100 Learner
##       Aktual  Prediksi
## 1     benign malignant
## 2     benign    benign
## 3     benign    benign
## 4     benign    benign
## 5     benign    benign
## 6  malignant malignant
## 7     benign    benign
## 8     benign    benign
## 9     benign    benign
## 10 malignant malignant
## 
## Hasil Prediksi - 200 Learner
##       Aktual  Prediksi
## 1     benign malignant
## 2     benign    benign
## 3     benign    benign
## 4     benign    benign
## 5     benign    benign
## 6  malignant malignant
## 7     benign    benign
## 8     benign    benign
## 9     benign    benign
## 10 malignant malignant
# A6. CONFUSION MATRIX
cm.ada <- list()

for (m in jumlah.learner) {
  
  nama <- as.character(m)
  
  cm.ada[[nama]] <- confusionMatrix(
    data = pred.ada[[nama]],
    reference = bc.test$Class,
    positive = "malignant"
  )
  
  cat("\nConfusion Matrix -", m, "Learner\n")
  print(cm.ada[[nama]])
}
## 
## Confusion Matrix - 50 Learner
## 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       
##                                           
## 
## Confusion Matrix - 100 Learner
## 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       
##                                           
## 
## Confusion Matrix - 200 Learner
## 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       
##

Setelah model terbentuk, prediksi dilakukan terhadap 204 observasi pada data uji. Hasil prediksi dari setiap model disimpan dan dibandingkan dengan kelas aktual. Perbandingan ini digunakan untuk mengetahui jumlah prediksi yang tepat dan kesalahan klasifikasi pada masing-masing kategori. Hasil prediksi selanjutnya menjadi dasar untuk menyusun confusion matrix dan menghitung metrik evaluasi.

Confusion matrix digunakan untuk melihat kesesuaian antara hasil prediksi dan kelas aktual. Pada model 50 dan 100 learner, terdapat 130 data benign yang diklasifikasikan dengan benar dan 67 data malignant yang diklasifikasikan dengan benar. Kesalahan klasifikasi terdiri atas 3 data benign yang diprediksi sebagai malignant dan 4 data malignant yang diprediksi sebagai benign. Sementara itu, model 200 learner mengklasifikasikan 130 data benign dan 66 data malignant dengan benar. Model ini salah mengklasifikasikan 3 data benign sebagai malignant dan 5 data malignant sebagai benign. Hasil tersebut menunjukkan bahwa model 50 dan 100 learner memiliki jumlah kesalahan lebih sedikit dibandingkan model 200 learner.

4.6 Perbandingan Metrik Evaluasi dan Grafik Performa AdaBoost

# A7. MENGHITUNG UKURAN EVALUASI KLASIFIKASI
evaluasi.ada <- data.frame()

for (m in jumlah.learner) {
  
  nama <- as.character(m)
  
  # Baris = aktual, kolom = prediksi
  tab.ada <- table(
    Aktual = bc.test$Class,
    Prediksi = pred.ada[[nama]]
  )
  
  cat("\nTabel Evaluasi -", m, "Learner\n")
  print(tab.ada)
  
  # True Negative, False Positive,
  # False Negative, True Positive
  TN <- tab.ada["benign", "benign"]
  FP <- tab.ada["benign", "malignant"]
  FN <- tab.ada["malignant", "benign"]
  TP <- tab.ada["malignant", "malignant"]
  
  # Accuracy
  Accuracy <- (TP + TN) /
    (TP + TN + FP + FN)
  
  # Precision
  Precision <- if ((TP + FP) == 0) {
    NA_real_
  } else {
    TP / (TP + FP)
  }
  
  # Recall / Sensitivity
  Recall <- if ((TP + FN) == 0) {
    NA_real_
  } else {
    TP / (TP + FN)
  }
  
  # Specificity
  Specificity <- if ((TN + FP) == 0) {
    NA_real_
  } else {
    TN / (TN + FP)
  }
  
  # F1-score
  F1 <- if (
    is.na(Precision) ||
    is.na(Recall) ||
    (Precision + Recall) == 0
  ) {
    NA_real_
  } else {
    2 * Precision * Recall /
      (Precision + Recall)
  }
  
  # Menyimpan hasil evaluasi
  evaluasi.ada <- rbind(
    evaluasi.ada,
    data.frame(
      Learner = m,
      Accuracy = Accuracy,
      Precision = Precision,
      Recall = Recall,
      Specificity = Specificity,
      F1_Score = F1
    )
  )
}
## 
## Tabel Evaluasi - 50 Learner
##            Prediksi
## Aktual      benign malignant
##   benign       130         3
##   malignant      4        67
## 
## Tabel Evaluasi - 100 Learner
##            Prediksi
## Aktual      benign malignant
##   benign       130         3
##   malignant      4        67
## 
## Tabel Evaluasi - 200 Learner
##            Prediksi
## Aktual      benign malignant
##   benign       130         3
##   malignant      5        66
# Tabel perbandingan performa
evaluasi.ada[, -1] <- round(
  evaluasi.ada[, -1],
  4
)

evaluasi.ada
##   Learner Accuracy Precision Recall Specificity F1_Score
## 1      50   0.9657    0.9571 0.9437      0.9774   0.9504
## 2     100   0.9657    0.9571 0.9437      0.9774   0.9504
## 3     200   0.9608    0.9565 0.9296      0.9774   0.9429
# A8. VISUALISASI PERBANDINGAN PERFORMA MODEL
# Mengambil lima ukuran evaluasi
hasil.plot <- as.matrix(
  evaluasi.ada[, c(
    "Accuracy",
    "Precision",
    "Recall",
    "Specificity",
    "F1_Score"
  )]
)

# Nama setiap model
rownames(hasil.plot) <- paste(
  evaluasi.ada$Learner,
  "Learner"
)

# Diagram batang
# Baris = jumlah learner
# Kolom = ukuran evaluasi
# Tidak menggunakan transpose
# Jumlah nama sumbu X = 5 kelompok evaluasi

barplot(
  hasil.plot,
  beside = TRUE,
  col = c("steelblue", "orange", "seagreen"),
  ylim = c(0, 1),
  ylab = "Nilai Evaluasi",
  xlab = "Ukuran Evaluasi",
  main = "Perbandingan Performa AdaBoost",
  names.arg = colnames(hasil.plot),
  legend.text = rownames(hasil.plot),
  args.legend = list(
    title = "Jumlah Learner",
    x = "topright",
    cex = 0.8
  )
)

Berdasarkan hasil, model 50 dan 100 learner menghasilkan akurasi tertinggi sebesar 96,57% dan F1-score sebesar 95,04%. Nilai precision kedua model mencapai 95,71%, sedangkan recall sebesar 94,37%. Sementara itu, model 200 learner memiliki akurasi 96,08% dan F1-score 94,29%, dengan recall sebesar 92,96%.

Ketiga model memiliki nilai specificity yang sama, yaitu 97,74%. Hal ini menunjukkan bahwa seluruh model memiliki kemampuan yang sama dalam mengenali kelas benign berdasarkan ukuran tersebut. Secara keseluruhan, grafik memperlihatkan bahwa model 50 dan 100 learner memberikan kinerja yang sama dan lebih baik pada sebagian besar metrik dibandingkan model 200 learner. Dengan demikian, penambahan jumlah learner tidak selalu meningkatkan performa klasifikasi.

4.7 Perbandingan Error

# A9. VISUALISASI ERROR KLASIFIKASI

# Menghitung error pada data training
error.training <- numeric(length(jumlah.learner))

# Menghitung error pada data testing
error.testing <- numeric(length(jumlah.learner))

for (i in seq_along(jumlah.learner)) {
  
  m <- jumlah.learner[i]
  nama <- as.character(m)
  
  # Error training
  pred.train.obj <- predict(
    model.ada[[nama]],
    newdata = bc.train
  )
  
  pred.train <- factor(
    pred.train.obj$class,
    levels = levels(bc.train$Class)
  )
  
  error.training[i] <- mean(
    pred.train != bc.train$Class
  )
  
  # Error testing
  error.testing[i] <- mean(
    pred.ada[[nama]] != bc.test$Class
  )
}

# Tabel error
tabel.error <- data.frame(
  Learner = jumlah.learner,
  Error_Training = round(error.training, 4),
  Error_Testing = round(error.testing, 4)
)

tabel.error
##   Learner Error_Training Error_Testing
## 1      50         0.0230        0.0343
## 2     100         0.0146        0.0343
## 3     200         0.0125        0.0392
# Grafik perbandingan error training dan testing
error.plot <- rbind(
  "Training" = error.training,
  "Testing" = error.testing
)

barplot(
  error.plot,
  beside = TRUE,
  col = c("steelblue", "tomato"),
  ylim = c(
    0,
    max(error.plot, na.rm = TRUE) * 1.2 + 0.01
  ),
  names.arg = as.character(jumlah.learner),
  xlab = "Jumlah Learner",
  ylab = "Proporsi Kesalahan",
  main = "Perbandingan Error AdaBoost",
  legend.text = rownames(error.plot),
  args.legend = list(
    title = "Data",
    x = "topright",
    cex = 0.8
  )
)

Nilai training error menurun dari 0,0230 pada 50 learner menjadi 0,0125 pada 200 learner. Namun, testing error pada model 50 dan 100 learner tetap sebesar 0,0343, sedangkan pada model 200 learner meningkat menjadi 0,0392. Hasil grafik menunjukkan bahwa penambahan learner dapat mengurangi kesalahan pada data latih, tetapi tidak selalu meningkatkan kemampuan model dalam memprediksi data baru.

4.8 Variabel Importance

# A10. VARIABLE IMPORTANCE

importance.ada <- list()

for (m in jumlah.learner) {
  
  nama <- as.character(m)
  
  # Mengambil variable importance
  imp <- model.ada[[nama]]$importance
  
  if (is.null(imp) || length(imp) == 0) {
    
    warning(
      paste("Variable importance tidak tersedia untuk",
            m, "learner.")
    )
    
    next
  }
  
  # Mengurutkan dari nilai terbesar
  importance.ada[[nama]] <- sort(
    imp,
    decreasing = TRUE
  )
  
  cat("\nVariable Importance -", m, "Learner\n")
  print(importance.ada[[nama]])
}
## 
## Variable Importance - 50 Learner
##       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 
## 
## Variable Importance - 100 Learner
##       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 
## 
## Variable Importance - 200 Learner
##       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 untuk setiap model
for (m in jumlah.learner) {
  
  nama <- as.character(m)
  
  if (is.null(importance.ada[[nama]])) {
    next
  }
  
  barplot(
    importance.ada[[nama]],
    las = 2,
    main = paste(
      "Variable Importance -",
      m, "Learner"
    ),
    ylab = "Importance",
    cex.names = 0.7
  )
}

Hasil variable importance digunakan untuk mengetahui variabel yang paling berkontribusi terhadap proses klasifikasi. Pada model 50 learner, variabel Cell.size memiliki nilai kepentingan tertinggi sebesar 41,86, diikuti Bare.nuclei sebesar 30,64 dan Cl.thickness sebesar 8,95. Pola yang sama terlihat pada model 100 dan 200 learner, dengan Cell.size dan Bare.nuclei tetap menjadi dua variabel terpenting. Pada model 100 learner, nilai kepentingan Cell.size sebesar 39,43 dan Bare.nuclei sebesar 29,38. Sementara itu, pada model 200 learner, nilainya masing-masing sebesar 37,36 dan 28,17. Hasil tersebut menunjukkan bahwa kedua variabel memiliki kontribusi terbesar secara konsisten pada ketiga model, sedangkan variabel lainnya memiliki nilai kepentingan yang lebih rendah.

4.9 Pemilihan Model Terbaik

# A11. MENENTUKAN MODEL TERBAIK BERDASARKAN F1-SCORE

if (all(is.na(evaluasi.ada$F1_Score))) {
  
  cat("\nF1-score tidak tersedia untuk menentukan model terbaik.\n")
  
} else {
  
  indeks.terbaik <- which.max(
    replace(
      evaluasi.ada$F1_Score,
      is.na(evaluasi.ada$F1_Score),
      -Inf
    )
  )
  
  model.terbaik <- evaluasi.ada[indeks.terbaik, ]
  
  cat("\nModel terbaik berdasarkan F1-score:\n")
  print(model.terbaik)
  
  cat(
    "\nJumlah learner terbaik:",
    model.terbaik$Learner,
    "\n"
  )
}
## 
## Model terbaik berdasarkan F1-score:
##   Learner Accuracy Precision Recall Specificity F1_Score
## 1      50   0.9657    0.9571 0.9437      0.9774   0.9504
## 
## Jumlah learner terbaik: 50

Berdasarkan hasil evaluasi, model 50 dan 100 learner memperoleh nilai F1-score tertinggi yang sama, yaitu 0,9504, dengan akurasi sebesar 96,57%. Kedua model juga memiliki nilai precision, recall, dan specificity yang identik. Oleh karena itu, model 50 learner dapat dipilih sebagai model terbaik apabila mempertimbangkan kesederhanaan model karena menghasilkan kinerja yang setara dengan jumlah learner lebih sedikit.

BAB V KESIMPULAN

5.1 Kesimpulan

Berdasarkan hasil dapat disimpulkan bahwa metode AdaBoost mampu mengklasifikasikan data BreastCancer dengan akurasi tertinggi sebesar 96,57%. Model 50 dan 100 learner memberikan hasil evaluasi terbaik, sedangkan model 200 learner menunjukkan peningkatan testing error dan penurunan beberapa metrik evaluasi. Selain itu, variabel Cell.size dan Bare.nuclei menjadi variabel yang paling berkontribusi dalam klasifikasi. Dengan mempertimbangkan kinerja dan jumlah learner, model 50 learner dapat dipilih sebagai model yang lebih sederhana tanpa mengurangi performa klasifikasinya.

DAFTAR PUSTAKA

Amatullah, F. F., Utami, H., Rizqi, T. A., Wahyuni, S. T., Sartono, B., & Firdawanti, A. R. (2025). Studi komparatif metode boosting dalam pengklasifikasian penerima bantuan Program Keluarga Harapan (PKH). TELKA.

Dinata, N. A. S., Abdurrahman, G., & Fitriyah, N. Q. (2023). Perbandingan optimasi algoritma Random Forest menggunakan teknik boosting terhadap kasus klasifikasi churn pelanggan di industri telekomunikasi. Jurnal Aplikasi Sistem Informasi dan Elektronika.

Fransiska, H. (2026). Modul Praktikum Machine Learning and Modern Prediction. Laboratorium Matematika Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Bengkulu.

Sihombing, P. R. (2025). Comparative analysis of ensemble bagging and boosting methods: A systematic literature review. Jurnal Bayesian: Jurnal Ilmiah Statistika dan Ekonometrika.

Sukmawati, C. E., Masruriyah, A. F., Juwita, A. R., Tejayanda, R. D., & Nurmayanti, T. (2024). Evaluasi kinerja algoritma AdaBoost dan XGBoost menggunakan dataset penyakit obesitas pada populasi dewasa. Jambura Journal of Informatics, 6(2).