BAB I PENDAHULUAN

1.1 Latar Belakang

Perkembangan teknologi informasi menyebabkan jumlah data yang tersedia semakin meningkat dan dapat dimanfaatkan untuk menyelesaikan berbagai permasalahan. Data tersebut dapat dianalisis menggunakan metode machine learning untuk membangun model klasifikasi maupun prediksi. Salah satu pendekatan yang dapat digunakan adalah ensemble learning, yaitu teknik yang menggabungkan beberapa model untuk menghasilkan prediksi yang lebih baik. Pendekatan ini membantu meningkatkan kemampuan model dalam mengenali pola data dan menghasilkan prediksi yang lebih akurat (Majid & Miharja, 2022).

Boosting merupakan metode ensemble learning yang membentuk model secara bertahap dengan memperhatikan kesalahan yang dihasilkan pada tahap sebelumnya. Proses ini dilakukan dengan membangun model baru yang berfokus pada kesalahan prediksi sehingga kelemahan model sebelumnya dapat diperbaiki. Metode boosting memiliki beberapa algoritma, di antaranya Adaptive Boosting (AdaBoost) dan Extreme Gradient Boosting (XGBoost), yang dapat digunakan untuk membangun model klasifikasi maupun regresi. Kinerja metode ini dapat dipengaruhi oleh karakteristik data, pemilihan algoritma, dan pengaturan parameter yang digunakan (Afrizal, 2022).

Boosting dilakukan untuk memahami konsep dasar dan tahapan penerapan metode boosting dalam analisis data. Kegiatan ini meliputi pembentukan model dan evaluasi kinerja untuk mengetahui kemampuan model dalam menghasilkan prediksi. Melalui praktikum ini, mahasiswa diharapkan mampu memahami cara kerja metode boosting serta penerapannya dalam menyelesaikan permasalahan klasifikasi maupun regresi.

1.2 Rumusan Masalah

Berdasarkan latar belakang tersebut, rumusan masalah dalam praktikum ini adalah sebagai berikut:

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

1.3 Tujuan

Tujuan yang ingin dicapai dalam praktikum ini adalah sebagai berikut:

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

BAB II TINJAUAN PUSTAKA

2.1 Boosting

Boosting merupakan metode ensemble learning yang membentuk model secara berurutan, dengan setiap model baru berusaha memperbaiki kesalahan model sebelumnya. Pada proses pembentukan model, perhatian diberikan pada observasi yang belum diprediksi dengan baik sehingga model berikutnya dapat mempelajari pola yang sebelumnya sulit dikenali. Hasil prediksi akhir diperoleh dengan menggabungkan prediksi dari model-model yang telah dibentuk. Hasil akhir diperoleh melalui penggabungan prediksi dari model-model yang dibentuk secara berurutan, dan penerapannya dapat digunakan dalam permasalahan klasifikasi maupun regresi (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).

Model-model sederhana yang dibangun pada setiap tahap tersebut disebut weak learner.Weak learner memiliki kemampuan prediksi yang terbatas apabila digunakan sendiri, tetapi setiap learner diarahkan untuk membantu memperbaiki kesalahan model yang telah terbentuk sebelumnya. Setelah sejumlah weak learner digabungkan, diperoleh model akhir yang lebih kuat, yang disebut strong learner. Pada Boosting berbasis pohon, decision tree sering digunakan sebagai base learner. Kedalaman pohon dapat dibatasi sehingga setiap pohon tetap sederhana dan berperan sebagai weak learner. Selanjutnya, kontribusi dari pohon-pohon tersebut digabungkan secara bertahap sehingga membentuk model Boosting akhir (Fransiska, 2026).

2.2 Adaptive Boosting

Adaptive Boosting atau AdaBoost merupakan algoritma boosting yang menggabungkan beberapa model dasar, umumnya decision stump atau pohon keputusan dengan satu pemisahan. Pada setiap iterasi, bobot observasi yang salah diklasifikasikan akan ditingkatkan sehingga model berikutnya lebih memperhatikan observasi tersebut. Hasil akhir klasifikasi ditentukan berdasarkan gabungan prediksi model dengan bobot yang sesuai (Abdurrahman, 2022).

Untuk klasifikasi biner, misalkan tersedia data latih (𝑥𝑖, 𝑦𝑖), 𝑖 = 1,2,…,𝑛. Secara sederhana, proses AdaBoost dilakukan sebagai berikut (Fransiska, 2026):

  1. Memberikan bobot awal yang sama, yaitu pada awal proses, semua observasi dianggap sama penting sehingga diberi bobot yang sama.

  2. Membentuk weak learner, yaitu pada setiap iterasi dibentuk weak learner 𝐺𝑚(𝑥). Setelah prediksi dilakukan, dihitung kesalahan klasifikasi terbobot:

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

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

  2. Menggabungkan learner dan setiap learner diberi bobot.

2.3 Gradient Boosting

Gradient Boosting merupakan algoritma yang membangun model secara bertahap dengan meminimalkan fungsi kerugian (loss function). Model baru dibentuk untuk memperkirakan arah perbaikan terhadap kesalahan model sebelumnya berdasarkan gradien fungsi kerugian. Proses tersebut dilakukan berulang kali sampai mencapai jumlah iterasi yang ditentukan atau kriteria penghentian tertentu. Algoritma ini dapat digunakan untuk klasifikasi dan regresi dengan menyesuaikan fungsi kerugian terhadap jenis permasalahan. Beberapa parameter yang memengaruhi kinerja Gradient Boosting adalah jumlah pohon, kedalaman pohon, dan learning rate (Fallahi, Nurdiati, dan Bukhari, 2023).

BAB III METODE PENELITIAN

3.1 Sumber Data

Jenis data yang digunakan dalam praktikum ini adalah data kategorik. Data kategorik adalah tipe data yang merepresentasikan karakteristik atau atribut dari objek yang diklasifikasikan ke dalam kategori tertentu. Sumber data yang digunakan merupakan data sekunder, yaitu data yang telah tersedia dan diperoleh melalui sumber yang sudah ada. Data yang digunakan adalah dataset BreastCancer dari package mlbench pada program R.

3.2 Variabel Penelitian

Variabel penelitian adalah segala sesuatu yang menjadi objek pengamatan dalam suatu penelitian. Variabel penelitian yang digunakan berasal dari dataset BreastCancer dengan jumlah 699 observasi dan 11 variabel. Variabel Class digunakan sebagai variabel respons dengan kategori benign dan malignant, sedangkan sembilan variabel prediktor yang digunakan adalah Clump Thickness, Uniformity of Cell Size, Uniformity of Cell Shape, Marginal Adhesion, Single Epithelial Cell Size, Bare Nuclei, Bland Chromatin, Normal Nucleoli dan Mitoses. Variabel Sample code number tidak digunakan dalam pemodelan karena hanya berfungsi sebagai identitas sampel.

3.3 Langkah-langkah Analisis

Berikut langkah-langkah analisis data pada penelitian ini:

  1. Memanggil package dan memuat data BreastCancer.
  2. Melakukan persiapan data dengan menghapus kolom Id, menangani missing value, dan memastikan variabel respons Class bertipe factor.
  3. Membagi data menjadi 70% data training dan 30% data testing menggunakan set.seed(234).
  4. Membentuk model AdaBoost menggunakan decision stump sebagai weak learner dengan jumlah learner sebanyak 50, 100, dan 200.
  5. Melakukan prediksi kelas pada data testing untuk masing-masing model.
  6. Membentuk confusion matrix untuk membandingkan kelas aktual dengan kelas hasil prediksi.
  7. Menghitung nilai Accuracy, Precision, Recall, Specificity, dan F1-score untuk setiap model.
  8. Membandingkan kinerja ketiga model berdasarkan hasil evaluasi.
  9. Menarik kesimpulan mengenai kinerja AdaBoost berdasarkan perbedaan jumlah learner yang digunakan.

BAB IV HASIL DAN PEMBAHASAN

4.1 Library

Library yang digunakan pada praktikum ini adalah adabag, caret, rpart, knitr dan mlbench. Package adabag digunakan untuk membangun model Boosting, sedangkan package mlbench menyediakan dataset BreastCancer yang digunakan dalam analisis klasifikasi.

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)
library(knitr)

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

Berdasarkan output str() dan summary(), dataset BreastCancer terdiri atas 699 observasi dan 11 variabel. Variabel Id berfungsi sebagai identitas, sembilan variabel lainnya merupakan prediktor yang menggambarkan karakteristik sel, sedangkan Class merupakan variabel target dengan dua kategori, yaitu benign dan malignant. Berdasarkan ringkasan data, terdapat 16 missing value pada variabel Bare.nuclei yang perlu ditangani sebelum pemodelan. Variabel Class terdiri atas 458 observasi benign (65,52%) dan 241 observasi malignant (34,48%).

4.2 Membarsihkan Data

## Menghapus kolom Id karena hanya sebagai identitas
bc$Id <- NULL

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

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

# Melihat kategori dan distribusi kelas
levels(bc$Class)
## [1] "benign"    "malignant"
table(bc$Class)
## 
##    benign malignant 
##       444       239
round(prop.table(table(bc$Class)), 4)
## 
##    benign malignant 
##    0.6501    0.3499

variabel Id dihapus karena hanya berfungsi sebagai identitas observasi, sedangkan observasi yang mengandung missing value dihapus sehingga jumlah data berkurang menjadi 683 observasi. Variabel Class dipastikan bertipe factor dengan dua kategori, yaitu benign dan malignant. Setelah prapemrosesan, terdapat 444 observasi benign (65,01%) dan 239 observasi malignant (34,99%). Hasil ini menunjukkan bahwa kategori benign lebih banyak dibandingkan kategori malignant dalam dataset yang digunakan untuk pemodelan.

4.3 Membagi Data Training dan Testing

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
cat("Jumlah data training:", nrow(bc.train), "\n")
## Jumlah data training: 479
cat("Jumlah data testing :", nrow(bc.test), "\n")
## Jumlah data testing : 204
# Distribusi kelas pada data training
table(bc.train$Class)
## 
##    benign malignant 
##       311       168
round(prop.table(table(bc.train$Class)), 4)
## 
##    benign malignant 
##    0.6493    0.3507
# Distribusi kelas pada data testing
table(bc.test$Class)
## 
##    benign malignant 
##       133        71
round(prop.table(table(bc.test$Class)), 4)
## 
##    benign malignant 
##     0.652     0.348

Berdasarkan pembagian data, diperoleh 479 observasi sebagai data training dan 204 observasi sebagai data testing. Data training terdiri atas 311 kategori benign (64,93%) dan 168 kategori malignant (35,07%), sedangkan data testing terdiri atas 133 kategori benign (65,20%) dan 71 kategori malignant (34,80%). Distribusi kelas pada kedua kelompok data cenderung sama, sehingga pembagian data cukup mempertahankan proporsi masing-masing kategori untuk proses pelatihan dan evaluasi model klasifikasi.

4.4 Pemodelan AdaBoost

Membentuk Model dengan 50, 100, dan 200 Learner

jumlah.learner <- c(50, 100, 200)
model.ada <- setNames(
  lapply(jumlah.learner, function(m) {
    set.seed(234)

    boosting(
      Class ~ .,
      data = bc.train,
      boos = FALSE,
      mfinal = m,
      coeflearn = "Freund",
      control = rpart.control(
        maxdepth = 1,
        minsplit = 10,
        cp = 0
      )
    )
  }),
  paste0("AdaBoost_", jumlah.learner)
)

model.ada$AdaBoost_50
## $formula
## Class ~ .
## <environment: 0x000002415a2b9380>
## 
## $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: 0x000002415a2b9380>
## 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" 
## 
## $call
## boosting(formula = Class ~ ., data = bc.train, boos = FALSE, 
##     mfinal = m, coeflearn = "Freund", control = rpart.control(maxdepth = 1, 
##         minsplit = 10, cp = 0))
## 
## attr(,"vardep.summary")
##    benign malignant 
##       311       168 
## attr(,"class")
## [1] "boosting"
model.ada$AdaBoost_100
## $formula
## Class ~ .
## <environment: 0x00000241565aa3f8>
## 
## $trees
## $trees[[1]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.35073070 benign (0.77410861 0.22589139)  
##   2) Cell.size=1,2 291 0.01461378 benign (0.98686034 0.01313966) *
##   3) Cell.size=3,4,5,6,7,8,9,10 188 0.05636743 malignant (0.23690224 0.76309776) *
## 
## $trees[[2]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.28384010 benign (0.8642427 0.1357573)  
##   2) Bare.nuclei=1,2,3,4 336 0.04044944 benign (0.9754087 0.0245913) *
##   3) Bare.nuclei=5,6,7,8,9,10 143 0.08027098 malignant (0.4541872 0.5458128) *
## 
## $trees[[3]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.30593700 benign (0.83731267 0.16268733)  
##   2) Bare.nuclei=1,5,7,8 321 0.09480714 benign (0.93600088 0.06399912) *
##   3) Bare.nuclei=2,3,4,6,9,10 158 0.08287293 malignant (0.47103720 0.52896280) *
## 
## $trees[[4]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.3951662 benign (0.7008386 0.2991614)  
##   2) Cell.size=1,2,3,4,5,6,7,8,9 430 0.2676721 benign (0.7757099 0.2242901) *
##   3) Cell.size=10 49 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[5]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4129529 malignant (0.3310271 0.6689729)  
##   2) Marg.adhesion=1,2 317 0.1051429 benign (0.6373274 0.3626726) *
##   3) Marg.adhesion=3,4,5,6,7,8,9,10 162 0.1502891 malignant (0.1799101 0.8200899) *
## 
## $trees[[6]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4705728 malignant (0.4413488 0.5586512)  
##   2) Cl.thickness=1,2,3,4,5,6,7,8 418 0.3588866 benign (0.5382002 0.4617998) *
##   3) Cl.thickness=9,10 61 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[7]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.3669965 malignant (0.2515718 0.7484282) *
## 
## $trees[[8]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.50000000 malignant (0.5000000 0.5000000)  
##   2) Normal.nucleoli=1,2,3,5,7,8 403 0.27951890 benign (0.6146659 0.3853341) *
##   3) Normal.nucleoli=4,6,9,10 76 0.05412526 malignant (0.1971013 0.8028987) *
## 
## $trees[[9]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.41567430 malignant (0.3360129 0.6639871)  
##   2) Bare.nuclei=2,3,5,7,8 89 0.26110990 benign (0.4744262 0.5255738) *
##   3) Bare.nuclei=1,4,6,9,10 390 0.08434473 malignant (0.1565713 0.8434287) *
## 
## $trees[[10]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.37517690 malignant (0.2649998 0.7350002)  
##   2) Cl.thickness=1,2,3 207 0.05427183 benign (0.6200856 0.3799144) *
##   3) Cl.thickness=4,5,6,7,8,9,10 272 0.22765340 malignant (0.1932780 0.8067220) *
## 
## $trees[[11]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.49353060 benign (0.5129366 0.4870634)  
##   2) Normal.nucleoli=2,7,8 52 0.05445584 benign (0.7985195 0.2014805) *
##   3) Normal.nucleoli=1,3,4,5,6,9,10 427 0.29616030 malignant (0.4090510 0.5909490) *
## 
## $trees[[12]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4157276 benign (0.6638891 0.3361109)  
##   2) Cl.thickness=1,2,3,4,5,6,7,8 418 0.3235771 benign (0.7173322 0.2826678) *
##   3) Cl.thickness=9,10 61 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[13]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4318839 malignant (0.3662502 0.6337498) *
## 
## $trees[[14]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.500000000 benign (0.50000000 0.50000000)  
##   2) Marg.adhesion=1,2,3,4,5,6 412 0.385276100 benign (0.56233109 0.43766891) *
##   3) Marg.adhesion=7,8,9,10 67 0.004984869 malignant (0.04164164 0.95835836) *
## 
## $trees[[15]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.412310400 malignant (0.32985405 0.67014595)  
##   2) Mitoses=1,2 418 0.410805200 malignant (0.37204469 0.62795531) *
##   3) Mitoses=3,4,5,6,7,8,10 61 0.001505178 malignant (0.01032388 0.98967612) *
## 
## $trees[[16]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.500000000 benign (0.5000000 0.5000000)  
##   2) Mitoses=1,2 418 0.413873600 benign (0.5462153 0.4537847) *
##   3) Mitoses=3,4,5,6,7,8,10 61 0.001825297 malignant (0.0207534 0.9792466) *
## 
## $trees[[17]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4284951000 malignant (0.359856375 0.640143625)  
##   2) Bl.cromatin=1 100 0.0002938377 benign (0.992079603 0.007920397) *
##   3) Bl.cromatin=2,3,4,5,6,7,8,9,10 379 0.3794064000 malignant (0.332445620 0.667554380) *
## 
## $trees[[18]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4608184 benign (0.5778849 0.4221151)  
##   2) Bare.nuclei=1,2,3,4,5,7,8 379 0.3954907 benign (0.6146671 0.3853329) *
##   3) Bare.nuclei=6,9,10 100 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[19]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.44596630 malignant (0.39318014 0.60681986)  
##   2) Bl.cromatin=1,2,3,4,7 411 0.43954650 malignant (0.43081988 0.56918012) *
##   3) Bl.cromatin=5,6,8,9,10 68 0.00641977 malignant (0.05631451 0.94368549) *
## 
## $trees[[20]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.500000000 malignant (0.50000000 0.50000000)  
##   2) Bl.cromatin=1,2,3,4,7 411 0.421850500 benign (0.53878624 0.46121376) *
##   3) Bl.cromatin=5,6,8,9,10 68 0.007197595 malignant (0.08433323 0.91566677) *
## 
## $trees[[21]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.43995 malignant (0.3816076 0.6183924) *
## 
## $trees[[22]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.500000000 benign (0.50000000 0.50000000)  
##   2) Marg.adhesion=1,2,3,4,5,6 412 0.423221100 benign (0.53754739 0.46245261) *
##   3) Marg.adhesion=7,8,9,10 67 0.008054692 malignant (0.09494695 0.90505305) *
## 
## $trees[[23]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4418372 malignant (0.3852275 0.6147725)  
##   2) Normal.nucleoli=2,3,4,7 84 0.1547312 benign (0.5325152 0.4674848) *
##   3) Normal.nucleoli=1,5,6,8,9,10 395 0.2191779 malignant (0.3007283 0.6992717) *
## 
## $trees[[24]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.470906900 malignant (0.44201016 0.55798984)  
##   2) Mitoses=1,2 418 0.447787100 benign (0.48272211 0.51727789) *
##   3) Mitoses=3,4,5,6,7,8,10 61 0.001400099 malignant (0.01509502 0.98490498) *
## 
## $trees[[25]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4277530 malignant (0.3584612 0.6415388)  
##   2) Cell.shape=1,2 286 0.0930425 benign (0.5697126 0.4302874) *
##   3) Cell.shape=3,4,5,6,7,8,9,10 193 0.2629486 malignant (0.2908636 0.7091364) *
## 
## $trees[[26]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.497271100 malignant (0.49454229 0.50545771)  
##   2) Epith.c.size=2,3,4,5,6,7,8,9,10 448 0.409440900 benign (0.54194665 0.45805335) *
##   3) Epith.c.size=1 31 0.007523409 malignant (0.07387815 0.92612185) *
## 
## $trees[[27]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4290196 malignant (0.3608437 0.6391563)  
##   2) Cl.thickness=1,2,3,4,5,6,7,8 418 0.4290196 malignant (0.3926055 0.6073945) *
##   3) Cl.thickness=9,10 61 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[28]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 malignant (0.5000000 0.5000000)  
##   2) Cl.thickness=1,2,3,4,5,6,7,8 418 0.4367134 benign (0.5337812 0.4662188) *
##   3) Cl.thickness=9,10 61 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[29]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.443823800 malignant (0.38904813 0.61095187)  
##   2) Mitoses=1,2 418 0.442614000 malignant (0.42081376 0.57918624) *
##   3) Mitoses=3,4,5,6,7,8,10 61 0.001209749 malignant (0.01359437 0.98640563) *
## 
## $trees[[30]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.500000000 malignant (0.50000000 0.50000000)  
##   2) Mitoses=1,2 418 0.437028000 benign (0.53292265 0.46707735) *
##   3) Mitoses=3,4,5,6,7,8,10 61 0.001362871 malignant (0.02118401 0.97881599) *
## 
## $trees[[31]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4454905 malignant (0.3922615 0.6077385) *
## 
## $trees[[32]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Epith.c.size=2,3,4,5,6,7,8,9,10 448 0.4258433 benign (0.5346919 0.4653081) *
##   3) Epith.c.size=1 31 0.0106576 malignant (0.1256580 0.8743420) *
## 
## $trees[[33]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4464078 malignant (0.3940330 0.6059670) *
## 
## $trees[[34]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 malignant (0.5000000 0.5000000)  
##   2) Cl.thickness=1,2,3,4,5,6,7,8 418 0.4496101 benign (0.5265319 0.4734681) *
##   3) Cl.thickness=9,10 61 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[35]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.454223500 malignant (0.40920794 0.59079206)  
##   2) Marg.adhesion=1,2,3,4,5,6 412 0.446288200 malignant (0.44281177 0.55718823) *
##   3) Marg.adhesion=7,8,9,10 67 0.007935268 malignant (0.07767819 0.92232181) *
## 
## $trees[[36]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.500000000 benign (0.5000000 0.5000000)  
##   2) Marg.adhesion=1,2,3,4,5,6 412 0.428161900 benign (0.5343165 0.4656835) *
##   3) Marg.adhesion=7,8,9,10 67 0.008734983 malignant (0.1084107 0.8915893) *
## 
## $trees[[37]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.44620890 malignant (0.3936487 0.6063513)  
##   2) Bare.nuclei=1,2,3,5,7 354 0.34702260 benign (0.4586714 0.5413286) *
##   3) Bare.nuclei=4,6,8,9,10 125 0.08128174 malignant (0.2405478 0.7594522) *
## 
## $trees[[38]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.41405 malignant (0.3330337 0.6669663) *
## 
## $trees[[39]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.500000000 malignant (0.50000000 0.50000000)  
##   2) Mitoses=1,2 418 0.446919300 benign (0.52712270 0.47287730) *
##   3) Mitoses=3,4,5,6,7,8,10 61 0.001813078 malignant (0.03302882 0.96697118) *
## 
## $trees[[40]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4538760 malignant (0.4085303 0.5914697)  
##   2) Normal.nucleoli=2,4,7,8 66 0.1176017 benign (0.5659454 0.4340546) *
##   3) Normal.nucleoli=1,3,5,6,9,10 413 0.2693754 malignant (0.3431565 0.6568435) *
## 
## $trees[[41]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4985351 malignant (0.4970702 0.5029298)  
##   2) Cl.thickness=1,2,3,4,5,6 371 0.3201975 benign (0.5759397 0.4240603) *
##   3) Cl.thickness=7,8,9,10 108 0.0611014 malignant (0.2509978 0.7490022) *
## 
## $trees[[42]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4336324000 malignant (0.369562990 0.630437010)  
##   2) Bl.cromatin=1 100 0.0001767088 benign (0.992607889 0.007392111) *
##   3) Bl.cromatin=2,3,4,5,6,7,8,9,10 379 0.4026408000 malignant (0.352531012 0.647468988) *
## 
## $trees[[43]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4742712 benign (0.5513218 0.4486782)  
##   2) Epith.c.size=2,3,4,5,6,7,8,9,10 448 0.4053968 benign (0.5833990 0.4166010) *
##   3) Epith.c.size=1 31 0.0135865 malignant (0.1794319 0.8205681) *
## 
## $trees[[44]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4569431 malignant (0.4145201 0.5854799)  
##   2) Marg.adhesion=1,2,3,4,5 398 0.4316513 malignant (0.4528515 0.5471485) *
##   3) Marg.adhesion=6,7,8,9,10 81 0.0252918 malignant (0.1695645 0.8304355) *
## 
## $trees[[45]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 5.000000e-01 benign (0.500000000 0.500000000)  
##   2) Epith.c.size=1,2,3,4,5,6,7 439 4.584224e-01 benign (0.521658880 0.478341120) *
##   3) Epith.c.size=8,9,10 40 6.367098e-05 malignant (0.001529035 0.998470965) *
## 
## $trees[[46]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4616793 malignant (0.4238061 0.5761939)  
##   2) Bare.nuclei=1,2,3,5,7 354 0.3570172 benign (0.4820857 0.5179143) *
##   3) Bare.nuclei=4,6,8,9,10 125 0.0741931 malignant (0.2597852 0.7402148) *
## 
## $trees[[47]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4266523 malignant (0.3563950 0.6436050) *
## 
## $trees[[48]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.50000000 benign (0.5000000 0.5000000)  
##   2) Marg.adhesion=1,2,3,4,5,6 412 0.42870390 benign (0.5327921 0.4672079) *
##   3) Marg.adhesion=7,8,9,10 67 0.01111687 malignant (0.1348922 0.8651078) *
## 
## $trees[[49]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4490011 malignant (0.3990524 0.6009476) *
## 
## $trees[[50]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.500000000 malignant (0.50000000 0.50000000)  
##   2) Mitoses=1,2 418 0.453915600 benign (0.52335752 0.47664248) *
##   3) Mitoses=3,4,5,6,7,8,10 61 0.001596736 malignant (0.03348782 0.96651218) *
## 
## $trees[[51]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4594337 malignant (0.4193979 0.5806021) *
## 
## $trees[[52]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Cell.shape=1,2,3,4,5,6,7,8 430 0.4592696 benign (0.5212299 0.4787701) *
##   3) Cell.shape=9,10 49 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[53]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4623376 malignant (0.4251003 0.5748997) *
## 
## $trees[[54]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Normal.nucleoli=1,2,3,4,5,6,7,8 432 0.4646206 benign (0.5183385 0.4816615) *
##   3) Normal.nucleoli=9,10 47 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[55]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.46695850 malignant (0.4342044 0.5657956)  
##   2) Marg.adhesion=1 270 0.08097315 benign (0.6077687 0.3922313) *
##   3) Marg.adhesion=2,3,4,5,6,7,8,9,10 209 0.32373320 malignant (0.3854989 0.6145011) *
## 
## $trees[[56]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4797413 benign (0.5404509 0.4595491)  
##   2) Bl.cromatin=1,2,4,7 300 0.2583496 benign (0.6233530 0.3766470) *
##   3) Bl.cromatin=3,5,6,8,9,10 179 0.1259875 malignant (0.3816213 0.6183787) *
## 
## $trees[[57]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.48410280 malignant (0.4682378 0.5317622)  
##   2) Epith.c.size=2,3,4,5,6,7,8,9,10 448 0.44086020 benign (0.5004751 0.4995249) *
##   3) Epith.c.size=1 31 0.01339468 malignant (0.1434737 0.8565263) *
## 
## $trees[[58]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4459963 malignant (0.3932380 0.6067620)  
##   2) Cl.thickness=1,2,3,4,5,6,7,8 418 0.4459963 malignant (0.4147214 0.5852786) *
##   3) Cl.thickness=9,10 61 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[59]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Cl.thickness=1,2,3,4,5,6,7,8 418 0.4573128 benign (0.5222953 0.4777047) *
##   3) Cl.thickness=9,10 61 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[60]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4606705 malignant (0.4218248 0.5781752) *
## 
## $trees[[61]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Cell.shape=1,2,3,4,5,6,7,8 430 0.4590236 benign (0.5213636 0.4786364) *
##   3) Cell.shape=9,10 49 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[62]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4621274 malignant (0.4246868 0.5753132)  
##   2) Cell.shape=1,2,3,4,5,6,7,8 430 0.4621274 malignant (0.4426167 0.5573833) *
##   3) Cell.shape=9,10 49 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[63]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.500000 malignant (0.5000000 0.5000000)  
##   2) Cell.shape=1,2,3,4,5,6,7,8 430 0.464794 benign (0.5182453 0.4817547) *
##   3) Cell.shape=9,10 49 0.000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[64]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.46710990 malignant (0.4345032 0.5654968)  
##   2) Normal.nucleoli=2,8 40 0.03795363 benign (0.6777694 0.3222306) *
##   3) Normal.nucleoli=1,3,4,5,6,7,9,10 439 0.37603740 malignant (0.3997537 0.6002463) *
## 
## $trees[[65]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.468133000 benign (0.56347614 0.43652386)  
##   2) Mitoses=1,2 418 0.427982800 benign (0.58443285 0.41556715) *
##   3) Mitoses=3,4,5,6,7,8,10 61 0.002098782 malignant (0.05606049 0.94393951) *
## 
## $trees[[66]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4672154 malignant (0.4347115 0.5652885)  
##   2) Bare.nuclei=1,2,3,5 347 0.2872640 benign (0.5096021 0.4903979) *
##   3) Bare.nuclei=4,6,7,8,9,10 132 0.1268085 malignant (0.3117331 0.6882669) *
## 
## $trees[[67]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.44360910 malignant (0.3886347 0.6113653)  
##   2) Epith.c.size=2,3,4,5,6,7,8,9,10 448 0.42926810 malignant (0.4191256 0.5808744) *
##   3) Epith.c.size=1 31 0.01434095 malignant (0.1223047 0.8776953) *
## 
## $trees[[68]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.50000000 benign (0.5000000 0.5000000)  
##   2) Epith.c.size=2,3,4,5,6,7,8,9,10 448 0.42626240 benign (0.5316304 0.4683696) *
##   3) Epith.c.size=1 31 0.01616395 malignant (0.1797962 0.8202038) *
## 
## $trees[[69]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4521438 malignant (0.4051564 0.5948436)  
##   2) Cell.shape=1,2,3,4,5,6,7,8 430 0.4521438 malignant (0.4251315 0.5748685) *
##   3) Cell.shape=9,10 49 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[70]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Cell.shape=1,2,3,4,5,6,7,8 430 0.4605059 benign (0.5205590 0.4794410) *
##   3) Cell.shape=9,10 49 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[71]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4633971 malignant (0.4271844 0.5728156) *
## 
## $trees[[72]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 malignant (0.5000000 0.5000000)  
##   2) Cl.thickness=1,2,3,4,5,6,7,8 418 0.4623138 benign (0.5195811 0.4804189) *
##   3) Cl.thickness=9,10 61 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[73]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4649552 malignant (0.4302530 0.5697470)  
##   2) Normal.nucleoli=1,2,3,4,5,6,7,8 432 0.4649552 malignant (0.4482473 0.5517527) *
##   3) Normal.nucleoli=9,10 47 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[74]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Normal.nucleoli=1,2,3,4,5,6,7,8 432 0.4647705 benign (0.5182580 0.4817420) *
##   3) Normal.nucleoli=9,10 47 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[75]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4670894 malignant (0.4344627 0.5655373)  
##   2) Cell.shape=1,2,3,4,5,6,7,8 430 0.4670894 malignant (0.4511898 0.5488102) *
##   3) Cell.shape=9,10 49 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[76]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Cell.shape=1,2,3,4,5,6,7,8 430 0.4672228 benign (0.5169440 0.4830560) *
##   3) Cell.shape=9,10 49 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[77]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4692393 malignant (0.4387106 0.5612894) *
## 
## $trees[[78]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 5.000000e-01 benign (0.500000000 0.500000000)  
##   2) Epith.c.size=1,2,3,4,5,6,7 439 4.676960e-01 benign (0.516669250 0.483330750) *
##   3) Epith.c.size=8,9,10 40 4.389648e-05 malignant (0.001357013 0.998642987) *
## 
## $trees[[79]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4697009 malignant (0.4396235 0.5603765)  
##   2) Cl.thickness=1,2,3,4,5,6,7,8 418 0.4697009 malignant (0.4558305 0.5441695) *
##   3) Cl.thickness=9,10 61 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[80]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Cl.thickness=1,2,3,4,5,6,7,8 418 0.4682758 benign (0.5163818 0.4836182) *
##   3) Cl.thickness=9,10 61 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[81]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4701686 malignant (0.4405487 0.5594513) *
## 
## $trees[[82]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 malignant (0.5000000 0.5000000)  
##   2) Normal.nucleoli=1,2,3,4,5,6,7,8 432 0.4690302 benign (0.5159798 0.4840202) *
##   3) Normal.nucleoli=9,10 47 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[83]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.47083660 malignant (0.4418709 0.5581291)  
##   2) Marg.adhesion=1 270 0.09051489 benign (0.5983161 0.4016839) *
##   3) Marg.adhesion=2,3,4,5,6,7,8,9,10 209 0.31931100 malignant (0.3930954 0.6069046) *
## 
## $trees[[84]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.48205730 benign (0.5358392 0.4641608)  
##   2) Mitoses=1,2 418 0.43957150 benign (0.5571979 0.4428021) *
##   3) Mitoses=3,4,5,6,7,8,10 61 0.00313354 malignant (0.0734267 0.9265733) *
## 
## $trees[[85]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4654212 malignant (0.4311716 0.5688284)  
##   2) Normal.nucleoli=1,2,3,4,5,6,7,8 432 0.4654212 malignant (0.4476842 0.5523158) *
##   3) Normal.nucleoli=9,10 47 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[86]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Normal.nucleoli=1,2,3,4,5,6,7,8 432 0.4675785 benign (0.5167539 0.4832461) *
##   3) Normal.nucleoli=9,10 47 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[87]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4695528 malignant (0.4393305 0.5606695)  
##   2) Bare.nuclei=1,2,3,4,5,7,8 379 0.4695528 malignant (0.4558817 0.5441183) *
##   3) Bare.nuclei=6,9,10 100 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[88]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Bare.nuclei=1,2,3,4,5,7,8 379 0.4676227 benign (0.5167303 0.4832697) *
##   3) Bare.nuclei=6,9,10 100 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[89]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4695918 malignant (0.4394077 0.5605923) *
## 
## $trees[[90]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 malignant (0.5000000 0.5000000)  
##   2) Cl.thickness=1,2,3,4,5,6,7,8 418 0.4681896 benign (0.5164278 0.4835722) *
##   3) Cl.thickness=9,10 61 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[91]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.47009230 malignant (0.4403979 0.5596021)  
##   2) Normal.nucleoli=2,7,8 52 0.06539554 benign (0.6281499 0.3718501) *
##   3) Normal.nucleoli=1,3,4,5,6,9,10 427 0.34556620 malignant (0.3975756 0.6024244) *
## 
## $trees[[92]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4738611 benign (0.5521353 0.4478647)  
##   2) Cell.shape=1,2,3,4,5,6,7,8 430 0.4380506 benign (0.5714775 0.4285225) *
##   3) Cell.shape=9,10 49 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[93]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4681373 malignant (0.4365323 0.5634677) *
## 
## $trees[[94]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.500000 benign (0.5000000 0.5000000)  
##   2) Cl.thickness=1,2,3,4,5,6,7,8 418 0.468919 benign (0.5160390 0.4839610) *
##   3) Cl.thickness=9,10 61 0.000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[95]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4707379 malignant (0.4416757 0.5583243)  
##   2) Cell.shape=1,2,3,4,5,6,7,8 430 0.4707379 malignant (0.4563860 0.5436140) *
##   3) Cell.shape=9,10 49 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[96]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Cell.shape=1,2,3,4,5,6,7,8 430 0.4711348 benign (0.5148616 0.4851384) *
##   3) Cell.shape=9,10 49 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[97]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4727103 malignant (0.4455826 0.5544174) *
## 
## $trees[[98]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Bare.nuclei=1,2,3,4,5,7,8 379 0.4710989 benign (0.5148806 0.4851194) *
##   3) Bare.nuclei=6,9,10 100 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[99]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4726782 malignant (0.4455190 0.5544810) *
## 
## $trees[[100]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.500000000 benign (0.50000000 0.50000000)  
##   2) Mitoses=1,2 418 0.461405900 benign (0.51810158 0.48189842) *
##   3) Mitoses=3,4,5,6,7,8,10 61 0.003930488 malignant (0.09242853 0.90757147) *
## 
## 
## $weights
##   [1] 2.5717138 1.9856256 1.5321449 1.0064655 1.0698480 0.5801999 0.5451234
##   [8] 0.6917486 0.6390794 0.9349320 0.6163319 0.7373809 0.2741688 0.4462154
##  [15] 0.3544226 0.3404553 0.4908206 0.4242897 0.2169821 0.2857359 0.2413651
##  [22] 0.2766479 0.5154829 0.2039552 0.5928070 0.3352479 0.2858522 0.2545115
##  [29] 0.2256576 0.2476953 0.2189081 0.2553753 0.2151954 0.2022461 0.1836203
##  [36] 0.2537656 0.2887726 0.3472479 0.2057939 0.4600370 0.4840385 0.3937390
##  [43] 0.3269484 0.1726552 0.1664390 0.2769147 0.2955228 0.2418894 0.2047074
##  [50] 0.1784223 0.1626228 0.1632832 0.1509353 0.1417547 0.3858930 0.4711798
##  [57] 0.1834937 0.2168607 0.1711655 0.1576435 0.1642741 0.1517813 0.1410573
##  [64] 0.3474907 0.2815185 0.3471548 0.2265274 0.2313205 0.1920127 0.1583061
##  [71] 0.1466740 0.1510314 0.1404095 0.1411518 0.1318331 0.1312970 0.1231983
##  [78] 0.1292198 0.1213451 0.1270675 0.1194677 0.1240381 0.3646852 0.2301910
##  [85] 0.1385364 0.1298683 0.1219398 0.1296906 0.1217831 0.1274138 0.3599910
##  [92] 0.2490773 0.1276238 0.1244847 0.1171821 0.1155893 0.1092676 0.1157334
##  [99] 0.1093963 0.1388773
## 
## $votes
##             [,1]      [,2]
##   [1,] 21.361932 11.897554
##   [2,] 21.403798 11.855687
##   [3,] 17.704267 15.555219
##   [4,]  9.110859 24.148627
##   [5,] 22.296864 10.962622
##   [6,] 21.833112 11.426374
##   [7,] 20.964478 12.295008
##   [8,] 16.238870 17.020615
##   [9,] 21.403798 11.855687
##  [10,]  7.461457 25.798028
##  [11,] 16.879609 16.379877
##  [12,] 21.833112 11.426374
##  [13,] 21.361932 11.897554
##  [14,] 21.361932 11.897554
##  [15,]  8.338035 24.921450
##  [16,] 10.966362 22.293123
##  [17,] 22.768044 10.491442
##  [18,] 22.296864 10.962622
##  [19,] 16.563249 16.696237
##  [20,] 21.833112 11.426374
##  [21,] 22.768044 10.491442
##  [22,] 21.435658 11.823828
##  [23,] 10.703282 22.556203
##  [24,] 21.546286 11.713200
##  [25,] 22.768044 10.491442
##  [26,] 22.768044 10.491442
##  [27,] 20.500726 12.758760
##  [28,] 16.089396 17.170090
##  [29,] 14.059070 19.200416
##  [30,]  9.081252 24.178234
##  [31,] 16.081326 17.178159
##  [32,]  9.478139 23.781347
##  [33,] 19.541506 13.717980
##  [34,] 16.487257 16.772229
##  [35,] 12.383365 20.876120
##  [36,] 11.763174 21.496312
##  [37,] 11.333578 21.925908
##  [38,] 11.379150 21.880335
##  [39,] 15.383063 17.876423
##  [40,] 13.308492 19.950994
##  [41,] 14.726538 18.532948
##  [42,] 21.874978 11.384508
##  [43,] 11.068957 22.190528
##  [44,] 16.075784 17.183701
##  [45,] 14.097367 19.162119
##  [46,] 21.361932 11.897554
##  [47,] 12.044044 21.215441
##  [48,] 13.984205 19.275280
##  [49,] 21.152278 12.107207
##  [50,]  9.353631 23.905855
##  [51,] 22.175237 11.084249
##  [52,] 18.510820 14.748666
##  [53,] 21.403798 11.855687
##  [54,] 21.435658 11.823828
##  [55,] 21.082533 12.176952
##  [56,] 21.361932 11.897554
##  [57,] 21.874978 11.384508
##  [58,] 11.866009 21.393476
##  [59,] 15.807480 17.452006
##  [60,] 21.361932 11.897554
##  [61,] 22.017465 11.242020
##  [62,] 22.296864 10.962622
##  [63,] 22.902025 10.357461
##  [64,] 22.296864 10.962622
##  [65,] 22.902025 10.357461
##  [66,] 21.361932 11.897554
##  [67,]  5.384133 27.875352
##  [68,] 12.367105 20.892381
##  [69,]  9.445124 23.814361
##  [70,] 15.472306 17.787180
##  [71,] 12.363520 20.895965
##  [72,] 23.283527  9.975959
##  [73,] 11.704780 21.554705
##  [74,] 18.311253 14.948232
##  [75,]  8.447312 24.812173
##  [76,] 18.832085 14.427401
##  [77,] 22.306057 10.953429
##  [78,] 19.119641 14.139845
##  [79,] 21.403798 11.855687
##  [80,] 20.468866 12.790619
##  [81,]  5.364796 27.894689
##  [82,] 22.768044 10.491442
##  [83,]  8.218373 25.041113
##  [84,] 11.456611 21.802875
##  [85,] 23.356944  9.902541
##  [86,] 21.240305 12.019181
##  [87,]  6.237359 27.022127
##  [88,] 25.067377  8.192109
##  [89,] 20.984349 12.275136
##  [90,] 21.833112 11.426374
##  [91,] 23.652603  9.606882
##  [92,] 23.652603  9.606882
##  [93,] 23.652603  9.606882
##  [94,] 11.230653 22.028833
##  [95,] 22.306057 10.953429
##  [96,] 22.768044 10.491442
##  [97,] 16.593556 16.665929
##  [98,] 10.925083 22.334403
##  [99,] 20.964478 12.295008
## [100,] 13.355887 19.903599
## [101,] 21.824606 11.434880
## [102,] 23.652603  9.606882
## [103,] 11.276659 21.982827
## [104,] 22.768044 10.491442
## [105,] 22.296864 10.962622
## [106,]  4.609944 28.649542
## [107,]  9.983226 23.276260
## [108,] 23.661265  9.598220
## [109,] 22.296864 10.962622
## [110,] 18.849665 14.409821
## [111,] 22.768200 10.491286
## [112,]  6.707011 26.552474
## [113,] 22.296864 10.962622
## [114,] 21.569639 11.689846
## [115,] 23.652603  9.606882
## [116,] 22.296864 10.962622
## [117,] 22.768044 10.491442
## [118,] 10.506884 22.752602
## [119,] 22.296864 10.962622
## [120,] 10.610770 22.648715
## [121,] 21.361932 11.897554
## [122,] 20.964478 12.295008
## [123,] 23.652603  9.606882
## [124,] 12.313171 20.946315
## [125,] 10.506884 22.752602
## [126,]  7.694714 25.564771
## [127,] 21.704057 11.555429
## [128,] 21.833112 11.426374
## [129,] 22.296864 10.962622
## [130,] 22.296864 10.962622
## [131,] 16.454276 16.805210
## [132,] 19.541506 13.717980
## [133,] 23.652603  9.606882
## [134,] 22.768044 10.491442
## [135,]  8.745240 24.514245
## [136,] 22.296864 10.962622
## [137,] 22.296864 10.962622
## [138,]  5.344500 27.914986
## [139,]  9.941549 23.317937
## [140,] 20.029546 13.229940
## [141,]  4.103546 29.155940
## [142,] 22.296864 10.962622
## [143,]  2.384960 30.874525
## [144,]  3.991766 29.267719
## [145,] 11.813585 21.445901
## [146,] 22.296864 10.962622
## [147,] 10.192717 23.066769
## [148,] 21.546286 11.713200
## [149,] 17.333753 15.925732
## [150,] 14.388509 18.870977
## [151,]  7.559737 25.699749
## [152,] 22.768044 10.491442
## [153,]  6.802821 26.456664
## [154,] 15.846538 17.412947
## [155,]  4.377813 28.881673
## [156,] 13.978676 19.280809
## [157,] 16.879609 16.379877
## [158,] 19.033401 14.226084
## [159,]  9.519430 23.740056
## [160,]  9.940089 23.319397
## [161,]  9.616714 23.642772
## [162,] 19.042296 14.217190
## [163,] 20.664582 12.594904
## [164,] 19.718288 13.541198
## [165,]  9.699782 23.559703
## [166,] 13.128529 20.130956
## [167,] 20.670183 12.589302
## [168,] 23.652603  9.606882
## [169,]  9.081252 24.178234
## [170,] 11.068957 22.190528
## [171,] 22.768044 10.491442
## [172,] 22.296864 10.962622
## [173,] 16.221794 17.037692
## [174,]  4.985956 28.273530
## [175,] 11.924766 21.334720
## [176,]  6.812865 26.446621
## [177,] 11.888542 21.370943
## [178,] 23.068458 10.191027
## [179,] 13.752618 19.506867
## [180,] 11.881504 21.377981
## [181,] 22.296864 10.962622
## [182,] 13.752618 19.506867
## [183,] 24.596197  8.663288
## [184,] 22.768044 10.491442
## [185,] 22.768044 10.491442
## [186,] 22.296864 10.962622
## [187,] 10.412318 22.847167
## [188,] 22.296864 10.962622
## [189,] 10.015588 23.243898
## [190,] 10.367292 22.892194
## [191,] 11.357952 21.901533
## [192,]  6.988291 26.271194
## [193,]  2.089301 31.170184
## [194,]  7.473850 25.785636
## [195,] 22.296864 10.962622
## [196,]  9.303781 23.955704
## [197,] 10.813091 22.446394
## [198,] 17.453461 15.806024
## [199,] 22.233633 11.025853
## [200,] 13.904662 19.354824
## [201,] 11.567693 21.691792
## [202,] 22.296864 10.962622
## [203,]  2.089301 31.170184
## [204,] 22.296864 10.962622
## [205,] 10.089585 23.169901
## [206,]  5.261648 27.997837
## [207,] 22.296864 10.962622
## [208,] 20.664582 12.594904
## [209,] 22.768044 10.491442
## [210,] 23.652603  9.606882
## [211,] 14.378769 18.880716
## [212,] 21.435658 11.823828
## [213,] 13.333169 19.926316
## [214,] 11.384781 21.874705
## [215,] 22.296864 10.962622
## [216,] 16.017101 17.242384
## [217,] 10.790510 22.468975
## [218,] 22.296864 10.962622
## [219,] 10.529320 22.730166
## [220,] 22.296864 10.962622
## [221,] 23.283527  9.975959
## [222,] 13.300101 19.959385
## [223,] 12.282780 20.976705
## [224,]  8.788298 24.471187
## [225,] 21.361932 11.897554
## [226,] 23.381867  9.877619
## [227,] 12.263321 20.996164
## [228,] 11.076391 22.183094
## [229,] 23.652603  9.606882
## [230,] 22.296864 10.962622
## [231,] 21.435658 11.823828
## [232,] 11.779283 21.480203
## [233,]  9.916976 23.342510
## [234,] 22.296864 10.962622
## [235,] 23.652603  9.606882
## [236,]  7.165798 26.093687
## [237,] 22.835700 10.423785
## [238,] 16.608882 16.650604
## [239,] 16.468741 16.790744
## [240,] 20.897245 12.362241
## [241,] 16.198282 17.061203
## [242,]  7.285898 25.973588
## [243,] 22.768044 10.491442
## [244,] 15.992920 17.266566
## [245,]  6.308847 26.950639
## [246,]  9.896835 23.362650
## [247,]  4.291926 28.967560
## [248,]  7.873962 25.385524
## [249,] 21.874978 11.384508
## [250,] 17.024947 16.234538
## [251,] 22.296864 10.962622
## [252,] 22.768044 10.491442
## [253,]  5.048841 28.210644
## [254,]  6.716934 26.542551
## [255,] 20.842851 12.416635
## [256,] 23.059796 10.199689
## [257,] 21.833112 11.426374
## [258,] 21.082533 12.176952
## [259,] 22.768044 10.491442
## [260,] 23.652603  9.606882
## [261,] 22.768044 10.491442
## [262,] 18.197411 15.062074
## [263,] 23.652603  9.606882
## [264,]  8.745240 24.514245
## [265,] 23.652603  9.606882
## [266,] 20.802736 12.456750
## [267,] 11.263800 21.995686
## [268,] 17.446833 15.812653
## [269,] 25.067377  8.192109
## [270,] 23.239380 10.020106
## [271,] 22.017465 11.242020
## [272,]  7.559737 25.699749
## [273,] 22.768044 10.491442
## [274,] 22.320217 10.939268
## [275,] 22.175237 11.084249
## [276,] 22.717671 10.541814
## [277,] 25.067377  8.192109
## [278,]  8.389445 24.870041
## [279,] 23.652603  9.606882
## [280,] 24.131510  9.127975
## [281,] 22.768044 10.491442
## [282,] 21.833112 11.426374
## [283,] 18.081506 15.177979
## [284,] 22.768044 10.491442
## [285,] 22.768044 10.491442
## [286,]  8.261713 24.997772
## [287,] 17.011658 16.247827
## [288,] 13.267475 19.992011
## [289,] 22.768044 10.491442
## [290,] 21.080889 12.178596
## [291,] 19.583372 13.676114
## [292,] 18.712894 14.546592
## [293,] 21.240305 12.019181
## [294,]  3.159295 30.100191
## [295,] 22.768044 10.491442
## [296,] 22.768044 10.491442
## [297,] 22.495663 10.763822
## [298,] 21.840839 11.418647
## [299,] 24.132445  9.127040
## [300,] 21.832177 11.427309
## [301,] 16.888110 16.371376
## [302,]  8.728925 24.530561
## [303,] 15.363552 17.895934
## [304,] 22.717671 10.541814
## [305,] 22.717671 10.541814
## [306,] 18.452258 14.807228
## [307,] 20.939111 12.320374
## [308,] 22.759538 10.499948
## [309,] 20.012685 13.246800
## [310,] 23.652603  9.606882
## [311,] 23.652603  9.606882
## [312,] 22.717671 10.541814
## [313,] 22.320217 10.939268
## [314,]  7.420959 25.838527
## [315,] 18.680300 14.579186
## [316,] 21.832177 11.427309
## [317,]  6.121116 27.138370
## [318,] 22.306057 10.953429
## [319,] 20.897245 12.362241
## [320,] 25.017005  8.242481
## [321,] 22.717671 10.541814
## [322,] 11.053074 22.206412
## [323,] 22.717671 10.541814
## [324,] 22.768044 10.491442
## [325,] 21.385285 11.874200
## [326,] 22.717671 10.541814
## [327,] 22.717671 10.541814
## [328,] 23.652603  9.606882
## [329,] 22.717671 10.541814
## [330,] 22.717671 10.541814
## [331,] 10.529320 22.730166
## [332,] 21.385285 11.874200
## [333,]  5.086166 28.173320
## [334,]  9.278323 23.981163
## [335,] 19.606726 13.652760
## [336,] 21.435658 11.823828
## [337,]  2.856606 30.402880
## [338,] 15.484104 17.775381
## [339,] 16.816411 16.443075
## [340,] 23.652603  9.606882
## [341,] 10.624954 22.634532
## [342,] 20.500726 12.758760
## [343,]  7.643055 25.616431
## [344,] 17.447621 15.811865
## [345,] 21.435658 11.823828
## [346,] 21.833112 11.426374
## [347,] 21.833112 11.426374
## [348,] 21.361932 11.897554
## [349,] 21.082533 12.176952
## [350,] 21.361932 11.897554
## [351,] 23.652603  9.606882
## [352,] 11.626654 21.632832
## [353,] 21.207616 12.051870
## [354,] 22.717671 10.541814
## [355,] 23.652603  9.606882
## [356,] 21.833112 11.426374
## [357,] 21.435658 11.823828
## [358,] 10.990923 22.268563
## [359,] 23.652603  9.606882
## [360,]  9.803849 23.455637
## [361,] 22.902025 10.357461
## [362,] 22.717671 10.541814
## [363,] 21.361932 11.897554
## [364,] 21.374286 11.885200
## [365,] 20.500726 12.758760
## [366,] 22.768044 10.491442
## [367,] 20.953479 12.306007
## [368,] 21.361932 11.897554
## [369,] 21.361932 11.897554
## [370,] 21.833112 11.426374
## [371,] 21.833112 11.426374
## [372,] 20.940046 12.319440
## [373,] 23.652603  9.606882
## [374,] 20.145957 13.113528
## [375,] 21.833112 11.426374
## [376,] 21.424658 11.834827
## [377,] 22.320217 10.939268
## [378,] 22.320217 10.939268
## [379,] 22.768044 10.491442
## [380,] 22.296864 10.962622
## [381,] 24.316799  8.942687
## [382,] 21.045248 12.214237
## [383,] 22.768044 10.491442
## [384,] 21.361932 11.897554
## [385,] 21.361932 11.897554
## [386,] 22.768044 10.491442
## [387,]  7.420959 25.838527
## [388,] 13.084226 20.175260
## [389,] 10.075662 23.183823
## [390,] 11.504093 21.755392
## [391,]  8.155438 25.104047
## [392,] 22.768044 10.491442
## [393,] 14.947402 18.312084
## [394,] 21.833112 11.426374
## [395,] 22.768044 10.491442
## [396,] 22.296864 10.962622
## [397,] 10.624954 22.634532
## [398,]  8.755896 24.503590
## [399,] 23.652603  9.606882
## [400,] 20.897245 12.362241
## [401,] 23.652603  9.606882
## [402,] 11.777041 21.482445
## [403,] 22.717671 10.541814
## [404,] 15.084783 18.174702
## [405,] 11.991022 21.268464
## [406,] 22.717671 10.541814
## [407,] 11.747838 21.511647
## [408,] 21.833112 11.426374
## [409,] 22.768044 10.491442
## [410,] 20.792478 12.467007
## [411,] 22.768044 10.491442
## [412,] 21.435658 11.823828
## [413,] 21.833112 11.426374
## [414,] 13.644169 19.615316
## [415,] 10.365211 22.894274
## [416,] 21.967093 11.292393
## [417,] 23.652603  9.606882
## [418,] 22.717671 10.541814
## [419,]  7.569780 25.689705
## [420,] 11.850315 21.409170
## [421,] 20.196330 13.063156
## [422,] 21.435658 11.823828
## [423,] 21.240305 12.019181
## [424,] 21.833112 11.426374
## [425,] 21.833112 11.426374
## [426,] 22.768044 10.491442
## [427,] 19.528647 13.730839
## [428,] 23.652603  9.606882
## [429,] 19.750148 13.509338
## [430,] 20.614809 12.644677
## [431,] 23.652603  9.606882
## [432,] 21.833112 11.426374
## [433,] 23.652603  9.606882
## [434,] 13.485583 19.773902
## [435,] 23.059796 10.199689
## [436,]  7.851255 25.408230
## [437,] 18.526813 14.732673
## [438,] 22.717671 10.541814
## [439,] 20.897245 12.362241
## [440,] 22.768044 10.491442
## [441,] 23.652603  9.606882
## [442,] 23.652603  9.606882
## [443,] 21.832177 11.427309
## [444,]  8.499186 24.760299
## [445,] 22.768044 10.491442
## [446,] 20.457038 12.802448
## [447,] 20.947617 12.311868
## [448,] 21.833112 11.426374
## [449,] 22.296864 10.962622
## [450,] 22.768044 10.491442
## [451,] 21.833112 11.426374
## [452,] 10.517706 22.741779
## [453,] 22.768044 10.491442
## [454,] 21.361932 11.897554
## [455,] 22.175237 11.084249
## [456,] 20.947617 12.311868
## [457,] 21.967093 11.292393
## [458,] 22.296864 10.962622
## [459,] 10.925189 22.334297
## [460,] 21.546286 11.713200
## [461,] 22.296864 10.962622
## [462,] 20.145957 13.113528
## [463,] 22.768044 10.491442
## [464,] 22.124864 11.134621
## [465,] 22.768044 10.491442
## [466,] 23.652603  9.606882
## [467,] 23.652603  9.606882
## [468,]  1.628045 31.631441
## [469,]  5.124397 28.135088
## [470,] 23.652603  9.606882
## [471,] 23.652603  9.606882
## [472,] 23.283527  9.975959
## [473,] 22.717671 10.541814
## [474,] 21.825694 11.433791
## [475,] 13.656040 19.603446
## [476,] 23.652603  9.606882
## [477,] 22.768044 10.491442
## [478,] 11.840272 21.419213
## [479,] 15.028474 18.231012
## 
## $prob
##              [,1]      [,2]
##   [1,] 0.64228088 0.3577191
##   [2,] 0.64353967 0.3564603
##   [3,] 0.53230730 0.4676927
##   [4,] 0.27393265 0.7260674
##   [5,] 0.67039112 0.3296089
##   [6,] 0.65644766 0.3435523
##   [7,] 0.63033079 0.3696692
##   [8,] 0.48824779 0.5117522
##   [9,] 0.64353967 0.3564603
##  [10,] 0.22434073 0.7756593
##  [11,] 0.50751263 0.4924874
##  [12,] 0.65644766 0.3435523
##  [13,] 0.64228088 0.3577191
##  [14,] 0.64228088 0.3577191
##  [15,] 0.25069646 0.7493035
##  [16,] 0.32972135 0.6702787
##  [17,] 0.68455790 0.3154421
##  [18,] 0.67039112 0.3296089
##  [19,] 0.49800076 0.5019992
##  [20,] 0.65644766 0.3435523
##  [21,] 0.68455790 0.3154421
##  [22,] 0.64449757 0.3555024
##  [23,] 0.32181142 0.6781886
##  [24,] 0.64782378 0.3521762
##  [25,] 0.68455790 0.3154421
##  [26,] 0.68455790 0.3154421
##  [27,] 0.61638734 0.3836127
##  [28,] 0.48375359 0.5162464
##  [29,] 0.42270858 0.5772914
##  [30,] 0.27304246 0.7269575
##  [31,] 0.48351097 0.5164890
##  [32,] 0.28497552 0.7150245
##  [33,] 0.58754684 0.4124532
##  [34,] 0.49571592 0.5042841
##  [35,] 0.37232582 0.6276742
##  [36,] 0.35367876 0.6463212
##  [37,] 0.34076227 0.6592377
##  [38,] 0.34213248 0.6578675
##  [39,] 0.46251655 0.5374834
##  [40,] 0.40014124 0.5998588
##  [41,] 0.44277707 0.5572229
##  [42,] 0.65770645 0.3422936
##  [43,] 0.33280603 0.6671940
##  [44,] 0.48334434 0.5166557
##  [45,] 0.42386003 0.5761400
##  [46,] 0.64228088 0.3577191
##  [47,] 0.36212359 0.6378764
##  [48,] 0.42045766 0.5795423
##  [49,] 0.63597731 0.3640227
##  [50,] 0.28123198 0.7187680
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##  [57,] 0.65770645 0.3422936
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##  [67,] 0.16188264 0.8381174
##  [68,] 0.37183693 0.6281631
##  [69,] 0.28398287 0.7160171
##  [70,] 0.46519979 0.5348002
##  [71,] 0.37172915 0.6282708
##  [72,] 0.70005672 0.2999433
##  [73,] 0.35192307 0.6480769
##  [74,] 0.55055732 0.4494427
##  [75,] 0.25398205 0.7460180
##  [76,] 0.56621695 0.4337830
##  [77,] 0.67066753 0.3293325
##  [78,] 0.57486279 0.4251372
##  [79,] 0.64353967 0.3564603
##  [80,] 0.61542943 0.3845706
##  [81,] 0.16130124 0.8386988
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##  [83,] 0.24709861 0.7529014
##  [84,] 0.34446145 0.6555385
##  [85,] 0.70226415 0.2977359
##  [86,] 0.63862397 0.3613760
##  [87,] 0.18753623 0.8124638
##  [88,] 0.75369106 0.2463089
##  [89,] 0.63092825 0.3690717
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##  [91,] 0.71115361 0.2888464
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##  [95,] 0.67066753 0.3293325
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##  [97,] 0.49891200 0.5010880
##  [98,] 0.32848021 0.6715198
##  [99,] 0.63033079 0.3696692
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## [151,] 0.22729567 0.7727043
## [152,] 0.68455790 0.3154421
## [153,] 0.20453778 0.7954622
## [154,] 0.47645170 0.5235483
## [155,] 0.13162599 0.8683740
## [156,] 0.42029142 0.5797086
## [157,] 0.50751263 0.4924874
## [158,] 0.57226987 0.4277301
## [159,] 0.28621698 0.7137830
## [160,] 0.29886479 0.7011352
## [161,] 0.28914199 0.7108580
## [162,] 0.57253729 0.4274627
## [163,] 0.62131393 0.3786861
## [164,] 0.59286209 0.4071379
## [165,] 0.29163958 0.7083604
## [166,] 0.39473037 0.6052696
## [167,] 0.62148235 0.3785177
## [168,] 0.71115361 0.2888464
## [169,] 0.27304246 0.7269575
## [170,] 0.33280603 0.6671940
## [171,] 0.68455790 0.3154421
## [172,] 0.67039112 0.3296089
## [173,] 0.48773436 0.5122656
## [174,] 0.14991079 0.8500892
## [175,] 0.35853730 0.6414627
## [176,] 0.20483975 0.7951603
## [177,] 0.35744817 0.6425518
## [178,] 0.69359035 0.3064096
## [179,] 0.41349462 0.5865054
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## [189,] 0.30113478 0.6988652
## [190,] 0.31170933 0.6882907
## [191,] 0.34149513 0.6585049
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## [199,] 0.66848997 0.3315100
## [200,] 0.41806606 0.5819339
## [201,] 0.34780133 0.6521987
## [202,] 0.67039112 0.3296089
## [203,] 0.06281821 0.9371818
## [204,] 0.67039112 0.3296089
## [205,] 0.30335961 0.6966404
## [206,] 0.15819994 0.8418001
## [207,] 0.67039112 0.3296089
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## [209,] 0.68455790 0.3154421
## [210,] 0.71115361 0.2888464
## [211,] 0.43232086 0.5676791
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## [213,] 0.40088321 0.5991168
## [214,] 0.34230178 0.6576982
## [215,] 0.67039112 0.3296089
## [216,] 0.48157995 0.5184201
## [217,] 0.32443407 0.6755659
## [218,] 0.67039112 0.3296089
## [219,] 0.31658097 0.6834190
## [220,] 0.67039112 0.3296089
## [221,] 0.70005672 0.2999433
## [222,] 0.39988896 0.6001110
## [223,] 0.36930157 0.6306984
## [224,] 0.26423435 0.7357657
## [225,] 0.64228088 0.3577191
## [226,] 0.70301349 0.2969865
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## [229,] 0.71115361 0.2888464
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## [233,] 0.29816986 0.7018301
## [234,] 0.67039112 0.3296089
## [235,] 0.71115361 0.2888464
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## [238,] 0.49937277 0.5006272
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## [240,] 0.62830932 0.3716907
## [241,] 0.48702745 0.5129725
## [242,] 0.21906225 0.7809378
## [243,] 0.68455790 0.3154421
## [244,] 0.48085289 0.5191471
## [245,] 0.18968565 0.8103144
## [246,] 0.29756429 0.7024357
## [247,] 0.12904366 0.8709563
## [248,] 0.23674335 0.7632566
## [249,] 0.65770645 0.3422936
## [250,] 0.51188246 0.4881175
## [251,] 0.67039112 0.3296089
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## [253,] 0.15180155 0.8481985
## [254,] 0.20195545 0.7980446
## [255,] 0.62667388 0.3733261
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## [258,] 0.63388032 0.3661197
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## [260,] 0.71115361 0.2888464
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## [262,] 0.54713447 0.4528655
## [263,] 0.71115361 0.2888464
## [264,] 0.26293973 0.7370603
## [265,] 0.71115361 0.2888464
## [266,] 0.62546776 0.3745322
## [267,] 0.33866428 0.6613357
## [268,] 0.52456713 0.4754329
## [269,] 0.75369106 0.2463089
## [270,] 0.69872937 0.3012706
## [271,] 0.66199056 0.3380094
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## [274,] 0.67109328 0.3289067
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## [284,] 0.68455790 0.3154421
## [285,] 0.68455790 0.3154421
## [286,] 0.24840172 0.7515983
## [287,] 0.51148290 0.4885171
## [288,] 0.39890799 0.6010920
## [289,] 0.68455790 0.3154421
## [290,] 0.63383089 0.3661691
## [291,] 0.58880562 0.4111944
## [292,] 0.56263330 0.4373667
## [293,] 0.63862397 0.3613760
## [294,] 0.09498928 0.9050107
## [295,] 0.68455790 0.3154421
## [296,] 0.68455790 0.3154421
## [297,] 0.67636835 0.3236317
## [298,] 0.65668000 0.3433200
## [299,] 0.72558083 0.2744192
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## [303,] 0.46192993 0.5380701
## [304,] 0.68304337 0.3169566
## [305,] 0.68304337 0.3169566
## [306,] 0.55479685 0.4452032
## [307,] 0.62956811 0.3704319
## [308,] 0.68430215 0.3156978
## [309,] 0.60171362 0.3982864
## [310,] 0.71115361 0.2888464
## [311,] 0.71115361 0.2888464
## [312,] 0.68304337 0.3169566
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## [315,] 0.56165329 0.4383467
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## [339,] 0.50561247 0.4943875
## [340,] 0.71115361 0.2888464
## [341,] 0.31945635 0.6805436
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## [350,] 0.64228088 0.3577191
## [351,] 0.71115361 0.2888464
## [352,] 0.34957407 0.6504259
## [353,] 0.63764113 0.3623589
## [354,] 0.68304337 0.3169566
## [355,] 0.71115361 0.2888464
## [356,] 0.65644766 0.3435523
## [357,] 0.64449757 0.3555024
## [358,] 0.33045979 0.6695402
## [359,] 0.71115361 0.2888464
## [360,] 0.29476850 0.7052315
## [361,] 0.68858626 0.3114137
## [362,] 0.68304337 0.3169566
## [363,] 0.64228088 0.3577191
## [364,] 0.64265233 0.3573477
## [365,] 0.61638734 0.3836127
## [366,] 0.68455790 0.3154421
## [367,] 0.63000008 0.3699999
## [368,] 0.64228088 0.3577191
## [369,] 0.64228088 0.3577191
## [370,] 0.65644766 0.3435523
## [371,] 0.65644766 0.3435523
## [372,] 0.62959621 0.3704038
## [373,] 0.71115361 0.2888464
## [374,] 0.60572066 0.3942793
## [375,] 0.65644766 0.3435523
## [376,] 0.64416686 0.3558331
## [377,] 0.67109328 0.3289067
## [378,] 0.67109328 0.3289067
## [379,] 0.68455790 0.3154421
## [380,] 0.67039112 0.3296089
## [381,] 0.73112372 0.2688763
## [382,] 0.63275929 0.3672407
## [383,] 0.68455790 0.3154421
## [384,] 0.64228088 0.3577191
## [385,] 0.64228088 0.3577191
## [386,] 0.68455790 0.3154421
## [387,] 0.22312308 0.7768769
## [388,] 0.39339833 0.6066017
## [389,] 0.30294101 0.6970590
## [390,] 0.34588909 0.6541109
## [391,] 0.24520639 0.7547936
## [392,] 0.68455790 0.3154421
## [393,] 0.44941772 0.5505823
## [394,] 0.65644766 0.3435523
## [395,] 0.68455790 0.3154421
## [396,] 0.67039112 0.3296089
## [397,] 0.31945635 0.6805436
## [398,] 0.26326010 0.7367399
## [399,] 0.71115361 0.2888464
## [400,] 0.62830932 0.3716907
## [401,] 0.71115361 0.2888464
## [402,] 0.35409571 0.6459043
## [403,] 0.68304337 0.3169566
## [404,] 0.45354831 0.5464517
## [405,] 0.36052938 0.6394706
## [406,] 0.68304337 0.3169566
## [407,] 0.35321768 0.6467823
## [408,] 0.65644766 0.3435523
## [409,] 0.68455790 0.3154421
## [410,] 0.62515935 0.3748406
## [411,] 0.68455790 0.3154421
## [412,] 0.64449757 0.3555024
## [413,] 0.65644766 0.3435523
## [414,] 0.41023393 0.5897661
## [415,] 0.31164677 0.6883532
## [416,] 0.66047603 0.3395240
## [417,] 0.71115361 0.2888464
## [418,] 0.68304337 0.3169566
## [419,] 0.22759764 0.7724024
## [420,] 0.35629882 0.6437012
## [421,] 0.60723518 0.3927648
## [422,] 0.64449757 0.3555024
## [423,] 0.63862397 0.3613760
## [424,] 0.65644766 0.3435523
## [425,] 0.65644766 0.3435523
## [426,] 0.68455790 0.3154421
## [427,] 0.58716022 0.4128398
## [428,] 0.71115361 0.2888464
## [429,] 0.59382000 0.4061800
## [430,] 0.61981744 0.3801826
## [431,] 0.71115361 0.2888464
## [432,] 0.65644766 0.3435523
## [433,] 0.71115361 0.2888464
## [434,] 0.40546579 0.5945342
## [435,] 0.69332991 0.3066701
## [436,] 0.23606063 0.7639394
## [437,] 0.55703847 0.4429615
## [438,] 0.68304337 0.3169566
## [439,] 0.62830932 0.3716907
## [440,] 0.68455790 0.3154421
## [441,] 0.71115361 0.2888464
## [442,] 0.71115361 0.2888464
## [443,] 0.65641956 0.3435804
## [444,] 0.25554173 0.7444583
## [445,] 0.68455790 0.3154421
## [446,] 0.61507379 0.3849262
## [447,] 0.62982385 0.3701761
## [448,] 0.65644766 0.3435523
## [449,] 0.67039112 0.3296089
## [450,] 0.68455790 0.3154421
## [451,] 0.65644766 0.3435523
## [452,] 0.31623178 0.6837682
## [453,] 0.68455790 0.3154421
## [454,] 0.64228088 0.3577191
## [455,] 0.66673420 0.3332658
## [456,] 0.62982385 0.3701761
## [457,] 0.66047603 0.3395240
## [458,] 0.67039112 0.3296089
## [459,] 0.32848340 0.6715166
## [460,] 0.64782378 0.3521762
## [461,] 0.67039112 0.3296089
## [462,] 0.60572066 0.3942793
## [463,] 0.68455790 0.3154421
## [464,] 0.66521968 0.3347803
## [465,] 0.68455790 0.3154421
## [466,] 0.71115361 0.2888464
## [467,] 0.71115361 0.2888464
## [468,] 0.04894978 0.9510502
## [469,] 0.15407326 0.8459267
## [470,] 0.71115361 0.2888464
## [471,] 0.71115361 0.2888464
## [472,] 0.70005672 0.2999433
## [473,] 0.68304337 0.3169566
## [474,] 0.65622465 0.3437753
## [475,] 0.41059083 0.5894092
## [476,] 0.71115361 0.2888464
## [477,] 0.68455790 0.3154421
## [478,] 0.35599685 0.6440031
## [479,] 0.45185528 0.5481447
## 
## $class
##   [1] "benign"    "benign"    "benign"    "malignant" "benign"    "benign"   
##   [7] "benign"    "malignant" "benign"    "malignant" "benign"    "benign"   
##  [13] "benign"    "benign"    "malignant" "malignant" "benign"    "benign"   
##  [19] "malignant" "benign"    "benign"    "benign"    "malignant" "benign"   
##  [25] "benign"    "benign"    "benign"    "malignant" "malignant" "malignant"
##  [31] "malignant" "malignant" "benign"    "malignant" "malignant" "malignant"
##  [37] "malignant" "malignant" "malignant" "malignant" "malignant" "benign"   
##  [43] "malignant" "malignant" "malignant" "benign"    "malignant" "malignant"
##  [49] "benign"    "malignant" "benign"    "benign"    "benign"    "benign"   
##  [55] "benign"    "benign"    "benign"    "malignant" "malignant" "benign"   
##  [61] "benign"    "benign"    "benign"    "benign"    "benign"    "benign"   
##  [67] "malignant" "malignant" "malignant" "malignant" "malignant" "benign"   
##  [73] "malignant" "benign"    "malignant" "benign"    "benign"    "benign"   
##  [79] "benign"    "benign"    "malignant" "benign"    "malignant" "malignant"
##  [85] "benign"    "benign"    "malignant" "benign"    "benign"    "benign"   
##  [91] "benign"    "benign"    "benign"    "malignant" "benign"    "benign"   
##  [97] "malignant" "malignant" "benign"    "malignant" "benign"    "benign"   
## [103] "malignant" "benign"    "benign"    "malignant" "malignant" "benign"   
## [109] "benign"    "benign"    "benign"    "malignant" "benign"    "benign"   
## [115] "benign"    "benign"    "benign"    "malignant" "benign"    "malignant"
## [121] "benign"    "benign"    "benign"    "malignant" "malignant" "malignant"
## [127] "benign"    "benign"    "benign"    "benign"    "malignant" "benign"   
## [133] "benign"    "benign"    "malignant" "benign"    "benign"    "malignant"
## [139] "malignant" "benign"    "malignant" "benign"    "malignant" "malignant"
## [145] "malignant" "benign"    "malignant" "benign"    "benign"    "malignant"
## [151] "malignant" "benign"    "malignant" "malignant" "malignant" "malignant"
## [157] "benign"    "benign"    "malignant" "malignant" "malignant" "benign"   
## [163] "benign"    "benign"    "malignant" "malignant" "benign"    "benign"   
## [169] "malignant" "malignant" "benign"    "benign"    "malignant" "malignant"
## [175] "malignant" "malignant" "malignant" "benign"    "malignant" "malignant"
## [181] "benign"    "malignant" "benign"    "benign"    "benign"    "benign"   
## [187] "malignant" "benign"    "malignant" "malignant" "malignant" "malignant"
## [193] "malignant" "malignant" "benign"    "malignant" "malignant" "benign"   
## [199] "benign"    "malignant" "malignant" "benign"    "malignant" "benign"   
## [205] "malignant" "malignant" "benign"    "benign"    "benign"    "benign"   
## [211] "malignant" "benign"    "malignant" "malignant" "benign"    "malignant"
## [217] "malignant" "benign"    "malignant" "benign"    "benign"    "malignant"
## [223] "malignant" "malignant" "benign"    "benign"    "malignant" "malignant"
## [229] "benign"    "benign"    "benign"    "malignant" "malignant" "benign"   
## [235] "benign"    "malignant" "benign"    "malignant" "malignant" "benign"   
## [241] "malignant" "malignant" "benign"    "malignant" "malignant" "malignant"
## [247] "malignant" "malignant" "benign"    "benign"    "benign"    "benign"   
## [253] "malignant" "malignant" "benign"    "benign"    "benign"    "benign"   
## [259] "benign"    "benign"    "benign"    "benign"    "benign"    "malignant"
## [265] "benign"    "benign"    "malignant" "benign"    "benign"    "benign"   
## [271] "benign"    "malignant" "benign"    "benign"    "benign"    "benign"   
## [277] "benign"    "malignant" "benign"    "benign"    "benign"    "benign"   
## [283] "benign"    "benign"    "benign"    "malignant" "benign"    "malignant"
## [289] "benign"    "benign"    "benign"    "benign"    "benign"    "malignant"
## [295] "benign"    "benign"    "benign"    "benign"    "benign"    "benign"   
## [301] "benign"    "malignant" "malignant" "benign"    "benign"    "benign"   
## [307] "benign"    "benign"    "benign"    "benign"    "benign"    "benign"   
## [313] "benign"    "malignant" "benign"    "benign"    "malignant" "benign"   
## [319] "benign"    "benign"    "benign"    "malignant" "benign"    "benign"   
## [325] "benign"    "benign"    "benign"    "benign"    "benign"    "benign"   
## [331] "malignant" "benign"    "malignant" "malignant" "benign"    "benign"   
## [337] "malignant" "malignant" "benign"    "benign"    "malignant" "benign"   
## [343] "malignant" "benign"    "benign"    "benign"    "benign"    "benign"   
## [349] "benign"    "benign"    "benign"    "malignant" "benign"    "benign"   
## [355] "benign"    "benign"    "benign"    "malignant" "benign"    "malignant"
## [361] "benign"    "benign"    "benign"    "benign"    "benign"    "benign"   
## [367] "benign"    "benign"    "benign"    "benign"    "benign"    "benign"   
## [373] "benign"    "benign"    "benign"    "benign"    "benign"    "benign"   
## [379] "benign"    "benign"    "benign"    "benign"    "benign"    "benign"   
## [385] "benign"    "benign"    "malignant" "malignant" "malignant" "malignant"
## [391] "malignant" "benign"    "malignant" "benign"    "benign"    "benign"   
## [397] "malignant" "malignant" "benign"    "benign"    "benign"    "malignant"
## [403] "benign"    "malignant" "malignant" "benign"    "malignant" "benign"   
## [409] "benign"    "benign"    "benign"    "benign"    "benign"    "malignant"
## [415] "malignant" "benign"    "benign"    "benign"    "malignant" "malignant"
## [421] "benign"    "benign"    "benign"    "benign"    "benign"    "benign"   
## [427] "benign"    "benign"    "benign"    "benign"    "benign"    "benign"   
## [433] "benign"    "malignant" "benign"    "malignant" "benign"    "benign"   
## [439] "benign"    "benign"    "benign"    "benign"    "benign"    "malignant"
## [445] "benign"    "benign"    "benign"    "benign"    "benign"    "benign"   
## [451] "benign"    "malignant" "benign"    "benign"    "benign"    "benign"   
## [457] "benign"    "benign"    "malignant" "benign"    "benign"    "benign"   
## [463] "benign"    "benign"    "benign"    "benign"    "benign"    "malignant"
## [469] "malignant" "benign"    "benign"    "benign"    "benign"    "benign"   
## [475] "malignant" "benign"    "benign"    "malignant" "malignant"
## 
## $importance
##     Bare.nuclei     Bl.cromatin      Cell.shape       Cell.size    Cl.thickness 
##       29.377745        2.153168        2.063869       39.432729        9.222131 
##    Epith.c.size   Marg.adhesion         Mitoses Normal.nucleoli 
##        1.906759        7.403588        2.924061        5.515949 
## 
## $terms
## Class ~ Cl.thickness + Cell.size + Cell.shape + Marg.adhesion + 
##     Epith.c.size + Bare.nuclei + Bl.cromatin + Normal.nucleoli + 
##     Mitoses
## attr(,"variables")
## list(Class, Cl.thickness, Cell.size, Cell.shape, Marg.adhesion, 
##     Epith.c.size, Bare.nuclei, Bl.cromatin, Normal.nucleoli, 
##     Mitoses)
## attr(,"factors")
##                 Cl.thickness Cell.size Cell.shape Marg.adhesion Epith.c.size
## Class                      0         0          0             0            0
## Cl.thickness               1         0          0             0            0
## Cell.size                  0         1          0             0            0
## Cell.shape                 0         0          1             0            0
## Marg.adhesion              0         0          0             1            0
## Epith.c.size               0         0          0             0            1
## Bare.nuclei                0         0          0             0            0
## Bl.cromatin                0         0          0             0            0
## Normal.nucleoli            0         0          0             0            0
## Mitoses                    0         0          0             0            0
##                 Bare.nuclei Bl.cromatin Normal.nucleoli Mitoses
## Class                     0           0               0       0
## Cl.thickness              0           0               0       0
## Cell.size                 0           0               0       0
## Cell.shape                0           0               0       0
## Marg.adhesion             0           0               0       0
## Epith.c.size              0           0               0       0
## Bare.nuclei               1           0               0       0
## Bl.cromatin               0           1               0       0
## Normal.nucleoli           0           0               1       0
## Mitoses                   0           0               0       1
## attr(,"term.labels")
## [1] "Cl.thickness"    "Cell.size"       "Cell.shape"      "Marg.adhesion"  
## [5] "Epith.c.size"    "Bare.nuclei"     "Bl.cromatin"     "Normal.nucleoli"
## [9] "Mitoses"        
## attr(,"order")
## [1] 1 1 1 1 1 1 1 1 1
## attr(,"intercept")
## [1] 1
## attr(,"response")
## [1] 1
## attr(,".Environment")
## <environment: 0x00000241565aa3f8>
## 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" 
## 
## $call
## boosting(formula = Class ~ ., data = bc.train, boos = FALSE, 
##     mfinal = m, coeflearn = "Freund", control = rpart.control(maxdepth = 1, 
##         minsplit = 10, cp = 0))
## 
## attr(,"vardep.summary")
##    benign malignant 
##       311       168 
## attr(,"class")
## [1] "boosting"
model.ada$AdaBoost_200
## $formula
## Class ~ .
## <environment: 0x000002415208ae78>
## 
## $trees
## $trees[[1]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.35073070 benign (0.77410861 0.22589139)  
##   2) Cell.size=1,2 291 0.01461378 benign (0.98686034 0.01313966) *
##   3) Cell.size=3,4,5,6,7,8,9,10 188 0.05636743 malignant (0.23690224 0.76309776) *
## 
## $trees[[2]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.28384010 benign (0.8642427 0.1357573)  
##   2) Bare.nuclei=1,2,3,4 336 0.04044944 benign (0.9754087 0.0245913) *
##   3) Bare.nuclei=5,6,7,8,9,10 143 0.08027098 malignant (0.4541872 0.5458128) *
## 
## $trees[[3]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.30593700 benign (0.83731267 0.16268733)  
##   2) Bare.nuclei=1,5,7,8 321 0.09480714 benign (0.93600088 0.06399912) *
##   3) Bare.nuclei=2,3,4,6,9,10 158 0.08287293 malignant (0.47103720 0.52896280) *
## 
## $trees[[4]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.3951662 benign (0.7008386 0.2991614)  
##   2) Cell.size=1,2,3,4,5,6,7,8,9 430 0.2676721 benign (0.7757099 0.2242901) *
##   3) Cell.size=10 49 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[5]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4129529 malignant (0.3310271 0.6689729)  
##   2) Marg.adhesion=1,2 317 0.1051429 benign (0.6373274 0.3626726) *
##   3) Marg.adhesion=3,4,5,6,7,8,9,10 162 0.1502891 malignant (0.1799101 0.8200899) *
## 
## $trees[[6]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4705728 malignant (0.4413488 0.5586512)  
##   2) Cl.thickness=1,2,3,4,5,6,7,8 418 0.3588866 benign (0.5382002 0.4617998) *
##   3) Cl.thickness=9,10 61 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[7]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.3669965 malignant (0.2515718 0.7484282) *
## 
## $trees[[8]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.50000000 malignant (0.5000000 0.5000000)  
##   2) Normal.nucleoli=1,2,3,5,7,8 403 0.27951890 benign (0.6146659 0.3853341) *
##   3) Normal.nucleoli=4,6,9,10 76 0.05412526 malignant (0.1971013 0.8028987) *
## 
## $trees[[9]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.41567430 malignant (0.3360129 0.6639871)  
##   2) Bare.nuclei=2,3,5,7,8 89 0.26110990 benign (0.4744262 0.5255738) *
##   3) Bare.nuclei=1,4,6,9,10 390 0.08434473 malignant (0.1565713 0.8434287) *
## 
## $trees[[10]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.37517690 malignant (0.2649998 0.7350002)  
##   2) Cl.thickness=1,2,3 207 0.05427183 benign (0.6200856 0.3799144) *
##   3) Cl.thickness=4,5,6,7,8,9,10 272 0.22765340 malignant (0.1932780 0.8067220) *
## 
## $trees[[11]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.49353060 benign (0.5129366 0.4870634)  
##   2) Normal.nucleoli=2,7,8 52 0.05445584 benign (0.7985195 0.2014805) *
##   3) Normal.nucleoli=1,3,4,5,6,9,10 427 0.29616030 malignant (0.4090510 0.5909490) *
## 
## $trees[[12]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4157276 benign (0.6638891 0.3361109)  
##   2) Cl.thickness=1,2,3,4,5,6,7,8 418 0.3235771 benign (0.7173322 0.2826678) *
##   3) Cl.thickness=9,10 61 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[13]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4318839 malignant (0.3662502 0.6337498) *
## 
## $trees[[14]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.500000000 benign (0.50000000 0.50000000)  
##   2) Marg.adhesion=1,2,3,4,5,6 412 0.385276100 benign (0.56233109 0.43766891) *
##   3) Marg.adhesion=7,8,9,10 67 0.004984869 malignant (0.04164164 0.95835836) *
## 
## $trees[[15]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.412310400 malignant (0.32985405 0.67014595)  
##   2) Mitoses=1,2 418 0.410805200 malignant (0.37204469 0.62795531) *
##   3) Mitoses=3,4,5,6,7,8,10 61 0.001505178 malignant (0.01032388 0.98967612) *
## 
## $trees[[16]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.500000000 benign (0.5000000 0.5000000)  
##   2) Mitoses=1,2 418 0.413873600 benign (0.5462153 0.4537847) *
##   3) Mitoses=3,4,5,6,7,8,10 61 0.001825297 malignant (0.0207534 0.9792466) *
## 
## $trees[[17]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4284951000 malignant (0.359856375 0.640143625)  
##   2) Bl.cromatin=1 100 0.0002938377 benign (0.992079603 0.007920397) *
##   3) Bl.cromatin=2,3,4,5,6,7,8,9,10 379 0.3794064000 malignant (0.332445620 0.667554380) *
## 
## $trees[[18]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4608184 benign (0.5778849 0.4221151)  
##   2) Bare.nuclei=1,2,3,4,5,7,8 379 0.3954907 benign (0.6146671 0.3853329) *
##   3) Bare.nuclei=6,9,10 100 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[19]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.44596630 malignant (0.39318014 0.60681986)  
##   2) Bl.cromatin=1,2,3,4,7 411 0.43954650 malignant (0.43081988 0.56918012) *
##   3) Bl.cromatin=5,6,8,9,10 68 0.00641977 malignant (0.05631451 0.94368549) *
## 
## $trees[[20]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.500000000 malignant (0.50000000 0.50000000)  
##   2) Bl.cromatin=1,2,3,4,7 411 0.421850500 benign (0.53878624 0.46121376) *
##   3) Bl.cromatin=5,6,8,9,10 68 0.007197595 malignant (0.08433323 0.91566677) *
## 
## $trees[[21]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.43995 malignant (0.3816076 0.6183924) *
## 
## $trees[[22]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.500000000 benign (0.50000000 0.50000000)  
##   2) Marg.adhesion=1,2,3,4,5,6 412 0.423221100 benign (0.53754739 0.46245261) *
##   3) Marg.adhesion=7,8,9,10 67 0.008054692 malignant (0.09494695 0.90505305) *
## 
## $trees[[23]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4418372 malignant (0.3852275 0.6147725)  
##   2) Normal.nucleoli=2,3,4,7 84 0.1547312 benign (0.5325152 0.4674848) *
##   3) Normal.nucleoli=1,5,6,8,9,10 395 0.2191779 malignant (0.3007283 0.6992717) *
## 
## $trees[[24]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.470906900 malignant (0.44201016 0.55798984)  
##   2) Mitoses=1,2 418 0.447787100 benign (0.48272211 0.51727789) *
##   3) Mitoses=3,4,5,6,7,8,10 61 0.001400099 malignant (0.01509502 0.98490498) *
## 
## $trees[[25]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4277530 malignant (0.3584612 0.6415388)  
##   2) Cell.shape=1,2 286 0.0930425 benign (0.5697126 0.4302874) *
##   3) Cell.shape=3,4,5,6,7,8,9,10 193 0.2629486 malignant (0.2908636 0.7091364) *
## 
## $trees[[26]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.497271100 malignant (0.49454229 0.50545771)  
##   2) Epith.c.size=2,3,4,5,6,7,8,9,10 448 0.409440900 benign (0.54194665 0.45805335) *
##   3) Epith.c.size=1 31 0.007523409 malignant (0.07387815 0.92612185) *
## 
## $trees[[27]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4290196 malignant (0.3608437 0.6391563)  
##   2) Cl.thickness=1,2,3,4,5,6,7,8 418 0.4290196 malignant (0.3926055 0.6073945) *
##   3) Cl.thickness=9,10 61 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[28]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 malignant (0.5000000 0.5000000)  
##   2) Cl.thickness=1,2,3,4,5,6,7,8 418 0.4367134 benign (0.5337812 0.4662188) *
##   3) Cl.thickness=9,10 61 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[29]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.443823800 malignant (0.38904813 0.61095187)  
##   2) Mitoses=1,2 418 0.442614000 malignant (0.42081376 0.57918624) *
##   3) Mitoses=3,4,5,6,7,8,10 61 0.001209749 malignant (0.01359437 0.98640563) *
## 
## $trees[[30]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.500000000 malignant (0.50000000 0.50000000)  
##   2) Mitoses=1,2 418 0.437028000 benign (0.53292265 0.46707735) *
##   3) Mitoses=3,4,5,6,7,8,10 61 0.001362871 malignant (0.02118401 0.97881599) *
## 
## $trees[[31]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4454905 malignant (0.3922615 0.6077385) *
## 
## $trees[[32]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Epith.c.size=2,3,4,5,6,7,8,9,10 448 0.4258433 benign (0.5346919 0.4653081) *
##   3) Epith.c.size=1 31 0.0106576 malignant (0.1256580 0.8743420) *
## 
## $trees[[33]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4464078 malignant (0.3940330 0.6059670) *
## 
## $trees[[34]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 malignant (0.5000000 0.5000000)  
##   2) Cl.thickness=1,2,3,4,5,6,7,8 418 0.4496101 benign (0.5265319 0.4734681) *
##   3) Cl.thickness=9,10 61 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[35]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.454223500 malignant (0.40920794 0.59079206)  
##   2) Marg.adhesion=1,2,3,4,5,6 412 0.446288200 malignant (0.44281177 0.55718823) *
##   3) Marg.adhesion=7,8,9,10 67 0.007935268 malignant (0.07767819 0.92232181) *
## 
## $trees[[36]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.500000000 benign (0.5000000 0.5000000)  
##   2) Marg.adhesion=1,2,3,4,5,6 412 0.428161900 benign (0.5343165 0.4656835) *
##   3) Marg.adhesion=7,8,9,10 67 0.008734983 malignant (0.1084107 0.8915893) *
## 
## $trees[[37]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.44620890 malignant (0.3936487 0.6063513)  
##   2) Bare.nuclei=1,2,3,5,7 354 0.34702260 benign (0.4586714 0.5413286) *
##   3) Bare.nuclei=4,6,8,9,10 125 0.08128174 malignant (0.2405478 0.7594522) *
## 
## $trees[[38]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.41405 malignant (0.3330337 0.6669663) *
## 
## $trees[[39]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.500000000 malignant (0.50000000 0.50000000)  
##   2) Mitoses=1,2 418 0.446919300 benign (0.52712270 0.47287730) *
##   3) Mitoses=3,4,5,6,7,8,10 61 0.001813078 malignant (0.03302882 0.96697118) *
## 
## $trees[[40]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4538760 malignant (0.4085303 0.5914697)  
##   2) Normal.nucleoli=2,4,7,8 66 0.1176017 benign (0.5659454 0.4340546) *
##   3) Normal.nucleoli=1,3,5,6,9,10 413 0.2693754 malignant (0.3431565 0.6568435) *
## 
## $trees[[41]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4985351 malignant (0.4970702 0.5029298)  
##   2) Cl.thickness=1,2,3,4,5,6 371 0.3201975 benign (0.5759397 0.4240603) *
##   3) Cl.thickness=7,8,9,10 108 0.0611014 malignant (0.2509978 0.7490022) *
## 
## $trees[[42]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4336324000 malignant (0.369562990 0.630437010)  
##   2) Bl.cromatin=1 100 0.0001767088 benign (0.992607889 0.007392111) *
##   3) Bl.cromatin=2,3,4,5,6,7,8,9,10 379 0.4026408000 malignant (0.352531012 0.647468988) *
## 
## $trees[[43]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4742712 benign (0.5513218 0.4486782)  
##   2) Epith.c.size=2,3,4,5,6,7,8,9,10 448 0.4053968 benign (0.5833990 0.4166010) *
##   3) Epith.c.size=1 31 0.0135865 malignant (0.1794319 0.8205681) *
## 
## $trees[[44]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4569431 malignant (0.4145201 0.5854799)  
##   2) Marg.adhesion=1,2,3,4,5 398 0.4316513 malignant (0.4528515 0.5471485) *
##   3) Marg.adhesion=6,7,8,9,10 81 0.0252918 malignant (0.1695645 0.8304355) *
## 
## $trees[[45]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 5.000000e-01 benign (0.500000000 0.500000000)  
##   2) Epith.c.size=1,2,3,4,5,6,7 439 4.584224e-01 benign (0.521658880 0.478341120) *
##   3) Epith.c.size=8,9,10 40 6.367098e-05 malignant (0.001529035 0.998470965) *
## 
## $trees[[46]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4616793 malignant (0.4238061 0.5761939)  
##   2) Bare.nuclei=1,2,3,5,7 354 0.3570172 benign (0.4820857 0.5179143) *
##   3) Bare.nuclei=4,6,8,9,10 125 0.0741931 malignant (0.2597852 0.7402148) *
## 
## $trees[[47]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4266523 malignant (0.3563950 0.6436050) *
## 
## $trees[[48]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.50000000 benign (0.5000000 0.5000000)  
##   2) Marg.adhesion=1,2,3,4,5,6 412 0.42870390 benign (0.5327921 0.4672079) *
##   3) Marg.adhesion=7,8,9,10 67 0.01111687 malignant (0.1348922 0.8651078) *
## 
## $trees[[49]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4490011 malignant (0.3990524 0.6009476) *
## 
## $trees[[50]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.500000000 malignant (0.50000000 0.50000000)  
##   2) Mitoses=1,2 418 0.453915600 benign (0.52335752 0.47664248) *
##   3) Mitoses=3,4,5,6,7,8,10 61 0.001596736 malignant (0.03348782 0.96651218) *
## 
## $trees[[51]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4594337 malignant (0.4193979 0.5806021) *
## 
## $trees[[52]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Cell.shape=1,2,3,4,5,6,7,8 430 0.4592696 benign (0.5212299 0.4787701) *
##   3) Cell.shape=9,10 49 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[53]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4623376 malignant (0.4251003 0.5748997) *
## 
## $trees[[54]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Normal.nucleoli=1,2,3,4,5,6,7,8 432 0.4646206 benign (0.5183385 0.4816615) *
##   3) Normal.nucleoli=9,10 47 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[55]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.46695850 malignant (0.4342044 0.5657956)  
##   2) Marg.adhesion=1 270 0.08097315 benign (0.6077687 0.3922313) *
##   3) Marg.adhesion=2,3,4,5,6,7,8,9,10 209 0.32373320 malignant (0.3854989 0.6145011) *
## 
## $trees[[56]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4797413 benign (0.5404509 0.4595491)  
##   2) Bl.cromatin=1,2,4,7 300 0.2583496 benign (0.6233530 0.3766470) *
##   3) Bl.cromatin=3,5,6,8,9,10 179 0.1259875 malignant (0.3816213 0.6183787) *
## 
## $trees[[57]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.48410280 malignant (0.4682378 0.5317622)  
##   2) Epith.c.size=2,3,4,5,6,7,8,9,10 448 0.44086020 benign (0.5004751 0.4995249) *
##   3) Epith.c.size=1 31 0.01339468 malignant (0.1434737 0.8565263) *
## 
## $trees[[58]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4459963 malignant (0.3932380 0.6067620)  
##   2) Cl.thickness=1,2,3,4,5,6,7,8 418 0.4459963 malignant (0.4147214 0.5852786) *
##   3) Cl.thickness=9,10 61 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[59]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Cl.thickness=1,2,3,4,5,6,7,8 418 0.4573128 benign (0.5222953 0.4777047) *
##   3) Cl.thickness=9,10 61 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[60]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4606705 malignant (0.4218248 0.5781752) *
## 
## $trees[[61]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Cell.shape=1,2,3,4,5,6,7,8 430 0.4590236 benign (0.5213636 0.4786364) *
##   3) Cell.shape=9,10 49 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[62]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4621274 malignant (0.4246868 0.5753132)  
##   2) Cell.shape=1,2,3,4,5,6,7,8 430 0.4621274 malignant (0.4426167 0.5573833) *
##   3) Cell.shape=9,10 49 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[63]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.500000 malignant (0.5000000 0.5000000)  
##   2) Cell.shape=1,2,3,4,5,6,7,8 430 0.464794 benign (0.5182453 0.4817547) *
##   3) Cell.shape=9,10 49 0.000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[64]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.46710990 malignant (0.4345032 0.5654968)  
##   2) Normal.nucleoli=2,8 40 0.03795363 benign (0.6777694 0.3222306) *
##   3) Normal.nucleoli=1,3,4,5,6,7,9,10 439 0.37603740 malignant (0.3997537 0.6002463) *
## 
## $trees[[65]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.468133000 benign (0.56347614 0.43652386)  
##   2) Mitoses=1,2 418 0.427982800 benign (0.58443285 0.41556715) *
##   3) Mitoses=3,4,5,6,7,8,10 61 0.002098782 malignant (0.05606049 0.94393951) *
## 
## $trees[[66]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4672154 malignant (0.4347115 0.5652885)  
##   2) Bare.nuclei=1,2,3,5 347 0.2872640 benign (0.5096021 0.4903979) *
##   3) Bare.nuclei=4,6,7,8,9,10 132 0.1268085 malignant (0.3117331 0.6882669) *
## 
## $trees[[67]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.44360910 malignant (0.3886347 0.6113653)  
##   2) Epith.c.size=2,3,4,5,6,7,8,9,10 448 0.42926810 malignant (0.4191256 0.5808744) *
##   3) Epith.c.size=1 31 0.01434095 malignant (0.1223047 0.8776953) *
## 
## $trees[[68]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.50000000 benign (0.5000000 0.5000000)  
##   2) Epith.c.size=2,3,4,5,6,7,8,9,10 448 0.42626240 benign (0.5316304 0.4683696) *
##   3) Epith.c.size=1 31 0.01616395 malignant (0.1797962 0.8202038) *
## 
## $trees[[69]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4521438 malignant (0.4051564 0.5948436)  
##   2) Cell.shape=1,2,3,4,5,6,7,8 430 0.4521438 malignant (0.4251315 0.5748685) *
##   3) Cell.shape=9,10 49 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[70]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Cell.shape=1,2,3,4,5,6,7,8 430 0.4605059 benign (0.5205590 0.4794410) *
##   3) Cell.shape=9,10 49 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[71]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4633971 malignant (0.4271844 0.5728156) *
## 
## $trees[[72]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 malignant (0.5000000 0.5000000)  
##   2) Cl.thickness=1,2,3,4,5,6,7,8 418 0.4623138 benign (0.5195811 0.4804189) *
##   3) Cl.thickness=9,10 61 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[73]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4649552 malignant (0.4302530 0.5697470)  
##   2) Normal.nucleoli=1,2,3,4,5,6,7,8 432 0.4649552 malignant (0.4482473 0.5517527) *
##   3) Normal.nucleoli=9,10 47 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[74]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Normal.nucleoli=1,2,3,4,5,6,7,8 432 0.4647705 benign (0.5182580 0.4817420) *
##   3) Normal.nucleoli=9,10 47 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[75]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4670894 malignant (0.4344627 0.5655373)  
##   2) Cell.shape=1,2,3,4,5,6,7,8 430 0.4670894 malignant (0.4511898 0.5488102) *
##   3) Cell.shape=9,10 49 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[76]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Cell.shape=1,2,3,4,5,6,7,8 430 0.4672228 benign (0.5169440 0.4830560) *
##   3) Cell.shape=9,10 49 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[77]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4692393 malignant (0.4387106 0.5612894) *
## 
## $trees[[78]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 5.000000e-01 benign (0.500000000 0.500000000)  
##   2) Epith.c.size=1,2,3,4,5,6,7 439 4.676960e-01 benign (0.516669250 0.483330750) *
##   3) Epith.c.size=8,9,10 40 4.389648e-05 malignant (0.001357013 0.998642987) *
## 
## $trees[[79]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4697009 malignant (0.4396235 0.5603765)  
##   2) Cl.thickness=1,2,3,4,5,6,7,8 418 0.4697009 malignant (0.4558305 0.5441695) *
##   3) Cl.thickness=9,10 61 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[80]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Cl.thickness=1,2,3,4,5,6,7,8 418 0.4682758 benign (0.5163818 0.4836182) *
##   3) Cl.thickness=9,10 61 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[81]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4701686 malignant (0.4405487 0.5594513) *
## 
## $trees[[82]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 malignant (0.5000000 0.5000000)  
##   2) Normal.nucleoli=1,2,3,4,5,6,7,8 432 0.4690302 benign (0.5159798 0.4840202) *
##   3) Normal.nucleoli=9,10 47 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[83]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.47083660 malignant (0.4418709 0.5581291)  
##   2) Marg.adhesion=1 270 0.09051489 benign (0.5983161 0.4016839) *
##   3) Marg.adhesion=2,3,4,5,6,7,8,9,10 209 0.31931100 malignant (0.3930954 0.6069046) *
## 
## $trees[[84]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.48205730 benign (0.5358392 0.4641608)  
##   2) Mitoses=1,2 418 0.43957150 benign (0.5571979 0.4428021) *
##   3) Mitoses=3,4,5,6,7,8,10 61 0.00313354 malignant (0.0734267 0.9265733) *
## 
## $trees[[85]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4654212 malignant (0.4311716 0.5688284)  
##   2) Normal.nucleoli=1,2,3,4,5,6,7,8 432 0.4654212 malignant (0.4476842 0.5523158) *
##   3) Normal.nucleoli=9,10 47 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[86]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Normal.nucleoli=1,2,3,4,5,6,7,8 432 0.4675785 benign (0.5167539 0.4832461) *
##   3) Normal.nucleoli=9,10 47 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[87]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4695528 malignant (0.4393305 0.5606695)  
##   2) Bare.nuclei=1,2,3,4,5,7,8 379 0.4695528 malignant (0.4558817 0.5441183) *
##   3) Bare.nuclei=6,9,10 100 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[88]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Bare.nuclei=1,2,3,4,5,7,8 379 0.4676227 benign (0.5167303 0.4832697) *
##   3) Bare.nuclei=6,9,10 100 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[89]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4695918 malignant (0.4394077 0.5605923) *
## 
## $trees[[90]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 malignant (0.5000000 0.5000000)  
##   2) Cl.thickness=1,2,3,4,5,6,7,8 418 0.4681896 benign (0.5164278 0.4835722) *
##   3) Cl.thickness=9,10 61 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[91]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.47009230 malignant (0.4403979 0.5596021)  
##   2) Normal.nucleoli=2,7,8 52 0.06539554 benign (0.6281499 0.3718501) *
##   3) Normal.nucleoli=1,3,4,5,6,9,10 427 0.34556620 malignant (0.3975756 0.6024244) *
## 
## $trees[[92]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4738611 benign (0.5521353 0.4478647)  
##   2) Cell.shape=1,2,3,4,5,6,7,8 430 0.4380506 benign (0.5714775 0.4285225) *
##   3) Cell.shape=9,10 49 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[93]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4681373 malignant (0.4365323 0.5634677) *
## 
## $trees[[94]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.500000 benign (0.5000000 0.5000000)  
##   2) Cl.thickness=1,2,3,4,5,6,7,8 418 0.468919 benign (0.5160390 0.4839610) *
##   3) Cl.thickness=9,10 61 0.000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[95]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4707379 malignant (0.4416757 0.5583243)  
##   2) Cell.shape=1,2,3,4,5,6,7,8 430 0.4707379 malignant (0.4563860 0.5436140) *
##   3) Cell.shape=9,10 49 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[96]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Cell.shape=1,2,3,4,5,6,7,8 430 0.4711348 benign (0.5148616 0.4851384) *
##   3) Cell.shape=9,10 49 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[97]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4727103 malignant (0.4455826 0.5544174) *
## 
## $trees[[98]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Bare.nuclei=1,2,3,4,5,7,8 379 0.4710989 benign (0.5148806 0.4851194) *
##   3) Bare.nuclei=6,9,10 100 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[99]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4726782 malignant (0.4455190 0.5544810) *
## 
## $trees[[100]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.500000000 benign (0.50000000 0.50000000)  
##   2) Mitoses=1,2 418 0.461405900 benign (0.51810158 0.48189842) *
##   3) Mitoses=3,4,5,6,7,8,10 61 0.003930488 malignant (0.09242853 0.90757147) *
## 
## $trees[[101]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4681313 malignant (0.4365205 0.5634795) *
## 
## $trees[[102]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Normal.nucleoli=1,2,3,4,5,6,7,8 432 0.4717815 benign (0.5145189 0.4854811) *
##   3) Normal.nucleoli=9,10 47 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[103]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4732890000 malignant (0.446730087 0.553269913)  
##   2) Bl.cromatin=1 100 0.0001840356 benign (0.991569811 0.008430189) *
##   3) Bl.cromatin=2,3,4,5,6,7,8,9,10 379 0.4491992000 malignant (0.433942966 0.566057034) *
## 
## $trees[[104]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 4.783295e-01 benign (0.543259831 0.456740169)  
##   2) Epith.c.size=1,2,3,4,5,6,7 439 4.523842e-01 benign (0.557043600 0.442956400) *
##   3) Epith.c.size=8,9,10 40 3.584781e-05 malignant (0.001504598 0.998495402) *
## 
## $trees[[105]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.47634880 malignant (0.4528032 0.5471968)  
##   2) Bare.nuclei=1,2,3,5,7,8 367 0.45611060 benign (0.4779874 0.5220126) *
##   3) Bare.nuclei=4,6,9,10 112 0.01723261 malignant (0.1883755 0.8116245) *
## 
## $trees[[106]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4540811000 malignant (0.40893026 0.59106974)  
##   2) Cl.thickness=1 99 0.0004003021 benign (0.97825287 0.02174713) *
##   3) Cl.thickness=2,3,4,5,6,7,8,9,10 380 0.4324324000 malignant (0.39735322 0.60264678) *
## 
## $trees[[107]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4813775 benign (0.5371934 0.4628066)  
##   2) Cell.shape=1,2,3,4,5,6,7,8 430 0.4556559 benign (0.5508147 0.4491853) *
##   3) Cell.shape=9,10 49 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[108]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4763737 malignant (0.4528527 0.5471473)  
##   2) Epith.c.size=2,3,4,5,6,7,8,9,10 448 0.4562903 benign (0.4778081 0.5221919) *
##   3) Epith.c.size=1 31 0.0174523 malignant (0.1908035 0.8091965) *
## 
## $trees[[109]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.454443400 malignant (0.40963691 0.59036309)  
##   2) Mitoses=1,2 418 0.451521700 malignant (0.42558096 0.57441904) *
##   3) Mitoses=3,4,5,6,7,8,10 61 0.002921701 malignant (0.06033172 0.93966828) *
## 
## $trees[[110]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.500000000 malignant (0.50000000 0.50000000)  
##   2) Mitoses=1,2 418 0.465259600 benign (0.51638479 0.48361521) *
##   3) Mitoses=3,4,5,6,7,8,10 61 0.003214594 malignant (0.08469487 0.91530513) *
## 
## $trees[[111]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.470751 malignant (0.4417016 0.5582984) *
## 
## $trees[[112]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Normal.nucleoli=1,2,3,4,5,6,7,8 432 0.4737123 benign (0.5134987 0.4865013) *
##   3) Normal.nucleoli=9,10 47 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[113]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4750254 malignant (0.4501750 0.5498250)  
##   2) Bare.nuclei=1,2,3,5 347 0.2954442 benign (0.5100261 0.4899739) *
##   3) Bare.nuclei=4,6,7,8,9,10 132 0.1351526 malignant (0.3475983 0.6524017) *
## 
## $trees[[114]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4553828 malignant (0.4114706 0.5885294) *
## 
## $trees[[115]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.50000000 benign (0.5000000 0.5000000)  
##   2) Epith.c.size=2,3,4,5,6,7,8,9,10 448 0.43347630 benign (0.5260495 0.4739505) *
##   3) Epith.c.size=1 31 0.01887385 malignant (0.2210117 0.7789883) *
## 
## $trees[[116]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4601264 malignant (0.4207567 0.5792433) *
## 
## $trees[[117]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.50000000 benign (0.5000000 0.5000000)  
##   2) Marg.adhesion=1,2,3,4,5,6 412 0.44170290 benign (0.5238081 0.4761919) *
##   3) Marg.adhesion=7,8,9,10 67 0.01412959 malignant (0.1950881 0.8049119) *
## 
## $trees[[118]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4619332 malignant (0.4243052 0.5756948)  
##   2) Cl.thickness=1,2,3,4,5,6,7,8 418 0.4619332 malignant (0.4385877 0.5614123) *
##   3) Cl.thickness=9,10 61 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[119]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.500000 benign (0.5000000 0.5000000)  
##   2) Cl.thickness=1,2,3,4,5,6,7,8 418 0.471717 benign (0.5145531 0.4854469) *
##   3) Cl.thickness=9,10 61 0.000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[120]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4732312 malignant (0.4466155 0.5533845) *
## 
## $trees[[121]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Normal.nucleoli=1,2,3,4,5,6,7,8 432 0.4727984 benign (0.5139811 0.4860189) *
##   3) Normal.nucleoli=9,10 47 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[122]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4742019 malignant (0.4485407 0.5514593) *
## 
## $trees[[123]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Cell.shape=1,2,3,4,5,6,7,8 430 0.4740866 benign (0.5133014 0.4866986) *
##   3) Cell.shape=9,10 49 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[124]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4753635 malignant (0.4508463 0.5491537) *
## 
## $trees[[125]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.500000000 benign (0.50000000 0.50000000)  
##   2) Mitoses=1,2,8 424 0.464865500 benign (0.51633172 0.48366828) *
##   3) Mitoses=3,4,5,6,7,10 55 0.003740883 malignant (0.09622752 0.90377248) *
## 
## $trees[[126]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 4.709327e-01 malignant (0.4420611239 0.5579388761)  
##   2) Epith.c.size=1,2,3,4,5,6,7 439 4.709056e-01 malignant (0.4550268964 0.5449731036) *
##   3) Epith.c.size=8,9,10 40 2.705246e-05 malignant (0.0008894451 0.9991105549) *
## 
## $trees[[127]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 5.000000e-01 benign (0.500000000 0.500000000)  
##   2) Epith.c.size=1,2,3,4,5,6,7 439 4.744372e-01 benign (0.513102335 0.486897665) *
##   3) Epith.c.size=8,9,10 40 2.872221e-05 malignant (0.001122334 0.998877666) *
## 
## $trees[[128]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4757095 malignant (0.4515334 0.5484666) *
## 
## $trees[[129]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Normal.nucleoli=1,2,3,4,5,6,7,8 432 0.4751366 benign (0.5127487 0.4872513) *
##   3) Normal.nucleoli=9,10 47 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[130]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4763144 malignant (0.4527348 0.5472652)  
##   2) Cl.thickness=1,2,3,4,5,6,7,8 418 0.4763144 malignant (0.4653456 0.5346544) *
##   3) Cl.thickness=9,10 61 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[131]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Cl.thickness=1,2,3,4,5,6,7,8 418 0.4752406 benign (0.5126940 0.4873060) *
##   3) Cl.thickness=9,10 61 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[132]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4764088000 malignant (0.452922482 0.547077518)  
##   2) Bl.cromatin=1 100 0.0001960756 benign (0.990082405 0.009917595) *
##   3) Bl.cromatin=2,3,4,5,6,7,8,9,10 379 0.4548958000 malignant (0.441592152 0.558407848) *
## 
## $trees[[133]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 4.804754e-01 benign (0.538989746 0.461010254)  
##   2) Epith.c.size=1,2,3,4,5,6,7 439 4.580792e-01 benign (0.550813127 0.449186873) *
##   3) Epith.c.size=8,9,10 40 2.781427e-05 malignant (0.001341053 0.998658947) *
## 
## $trees[[134]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4793656 malignant (0.4588014 0.5411986)  
##   2) Cell.shape=1,2,3,4,5,6,7,8 430 0.4793656 malignant (0.4706968 0.5293032) *
##   3) Cell.shape=9,10 49 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[135]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Cell.shape=1,2,3,4,5,6,7,8 430 0.4766518 benign (0.5119532 0.4880468) *
##   3) Cell.shape=9,10 49 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[136]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4776934 malignant (0.4554755 0.5445245)  
##   2) Bl.cromatin=1,2,3,7 381 0.3154826 benign (0.5081143 0.4918857) *
##   3) Bl.cromatin=4,5,6,8,9,10 98 0.1213663 malignant (0.3492494 0.6507506) *
## 
## $trees[[137]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4552801 malignant (0.4112700 0.5887300)  
##   2) Cell.shape=4,5,6,7,8,9,10 160 0.2954379 benign (0.4765298 0.5234702) *
##   3) Cell.shape=1,2,3 319 0.1335003 malignant (0.3092046 0.6907954) *
## 
## $trees[[138]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4373553 malignant (0.3766469 0.6233531) *
## 
## $trees[[139]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.50000000 benign (0.5000000 0.5000000)  
##   2) Cl.thickness=1,2,3,4,5,6 371 0.35027660 benign (0.5480219 0.4519781) *
##   3) Cl.thickness=7,8,9,10 108 0.07529076 malignant (0.3346046 0.6653954) *
## 
## $trees[[140]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.45813640 malignant (0.4168557 0.5831443)  
##   2) Cl.thickness=8,9,10 91 0.04133692 benign (0.6378581 0.3621419) *
##   3) Cl.thickness=1,2,3,4,5,6,7 388 0.37202150 malignant (0.3859055 0.6140945) *
## 
## $trees[[141]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4766048 benign (0.5466883 0.4533117)  
##   2) Cell.shape=1,2,3,4,5,6,7,8 430 0.4494252 benign (0.5611959 0.4388041) *
##   3) Cell.shape=9,10 49 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[142]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4753171 malignant (0.4507542 0.5492458)  
##   2) Normal.nucleoli=1,2,3,4,5,6,7,8 432 0.4753171 malignant (0.4649845 0.5350155) *
##   3) Normal.nucleoli=9,10 47 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[143]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 malignant (0.5000000 0.5000000)  
##   2) Normal.nucleoli=1,2,3,4,5,6,7,8 432 0.4721401 benign (0.5143291 0.4856709) *
##   3) Normal.nucleoli=9,10 47 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[144]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4736106 malignant (0.4473677 0.5526323) *
## 
## $trees[[145]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.50000000 malignant (0.5000000 0.5000000)  
##   2) Marg.adhesion=1,2,3,4,5,6 412 0.43468990 benign (0.5251848 0.4748152) *
##   3) Marg.adhesion=7,8,9,10 67 0.01919717 malignant (0.2271658 0.7728342) *
## 
## $trees[[146]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4613521 malignant (0.4231633 0.5768367) *
## 
## $trees[[147]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 5.000000e-01 benign (0.500000000 0.500000000)  
##   2) Epith.c.size=1,2,3,4,5,6,7 439 4.744029e-01 benign (0.513116906 0.486883094) *
##   3) Epith.c.size=8,9,10 40 3.572304e-05 malignant (0.001393645 0.998606355) *
## 
## $trees[[148]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4756855 malignant (0.4514857 0.5485143) *
## 
## $trees[[149]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Normal.nucleoli=1,2,3,4,5,6,7,8 432 0.4745167 benign (0.5130748 0.4869252) *
##   3) Normal.nucleoli=9,10 47 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[150]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4757525 malignant (0.4516189 0.5483811) *
## 
## $trees[[151]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 malignant (0.5000000 0.5000000)  
##   2) Cl.thickness=1,2,3,4,5,6,7,8 418 0.4746499 benign (0.5130047 0.4869953) *
##   3) Cl.thickness=9,10 61 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[152]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.475873100 malignant (0.45185831 0.54814169)  
##   2) Cl.thickness=1 99 0.000249881 benign (0.98721845 0.01278155) *
##   3) Cl.thickness=2,3,4,5,6,7,8,9,10 380 0.454615800 malignant (0.44068393 0.55931607) *
## 
## $trees[[153]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4807774 benign (0.5383885 0.4616115)  
##   2) Cell.shape=1,2,3,4,5,6,7,8 430 0.4579967 benign (0.5504278 0.4495722) *
##   3) Cell.shape=9,10 49 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[154]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4789848 malignant (0.4580436 0.5419564) *
## 
## $trees[[155]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 5.000000e-01 benign (0.500000000 0.500000000)  
##   2) Epith.c.size=1,2,3,4,5,6,7 439 4.764364e-01 benign (0.512049518 0.487950482) *
##   3) Epith.c.size=8,9,10 40 3.326538e-05 malignant (0.001409735 0.998590265) *
## 
## $trees[[156]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4775304 malignant (0.4551513 0.5448487) *
## 
## $trees[[157]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 malignant (0.5000000 0.5000000)  
##   2) Normal.nucleoli=1,2,3,4,5,6,7,8 432 0.4766149 benign (0.5119725 0.4880275) *
##   3) Normal.nucleoli=9,10 47 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[158]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.47765980 malignant (0.45540853 0.54459147)  
##   2) Mitoses=1,2 418 0.47425640 malignant (0.46985227 0.53014773) *
##   3) Mitoses=3,4,5,6,7,8,10 61 0.00340334 malignant (0.08619008 0.91380992) *
## 
## $trees[[159]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.500000000 malignant (0.5000000 0.5000000)  
##   2) Mitoses=1,2 418 0.468414700 benign (0.5145218 0.4854782) *
##   3) Mitoses=3,4,5,6,7,8,10 61 0.003562515 malignant (0.1013579 0.8986421) *
## 
## $trees[[160]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.473865 malignant (0.4478723 0.5521277) *
## 
## $trees[[161]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Cl.thickness=1,2,3,4,5,6,7,8 418 0.4778402 benign (0.5113310 0.4886690) *
##   3) Cl.thickness=9,10 61 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[162]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4787807 malignant (0.4576376 0.5423624)  
##   2) Bare.nuclei=1,2,3,4,5,7,8 379 0.4787807 malignant (0.4687518 0.5312482) *
##   3) Bare.nuclei=6,9,10 100 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[163]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 malignant (0.5000000 0.5000000)  
##   2) Bare.nuclei=1,2,3,4,5,7,8 379 0.4781417 benign (0.5111734 0.4888266) *
##   3) Bare.nuclei=6,9,10 100 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[164]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4790573 malignant (0.4581879 0.5418121) *
## 
## $trees[[165]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Normal.nucleoli=1,2,3,4,5,6,7,8 432 0.4783661 benign (0.5110562 0.4889438) *
##   3) Normal.nucleoli=9,10 47 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[166]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4792633 malignant (0.4585978 0.5414022) *
## 
## $trees[[167]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Bl.cromatin=1,2,3,4,5,7 437 0.4783651 benign (0.5110566 0.4889434) *
##   3) Bl.cromatin=6,8,9,10 42 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[168]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4792625 malignant (0.4585961 0.5414039) *
## 
## $trees[[169]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 5.000000e-01 benign (0.500000000 0.500000000)  
##   2) Epith.c.size=1,2,3,4,5,6,7 439 4.782261e-01 benign (0.511111026 0.488888974) *
##   3) Epith.c.size=8,9,10 40 3.651986e-05 malignant (0.001674422 0.998325578) *
## 
## $trees[[170]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4791715000 malignant (0.45841507 0.54158493)  
##   2) Bl.cromatin=1 100 0.0003253254 benign (0.98252513 0.01747487) *
##   3) Bl.cromatin=2,3,4,5,6,7,8,9,10 379 0.4592898000 malignant (0.44806870 0.55193130) *
## 
## $trees[[171]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4819581 benign (0.5360368 0.4639632)  
##   2) Epith.c.size=2,3,4,5,6,7,8,9,10 448 0.4195146 benign (0.5587201 0.4412799) *
##   3) Epith.c.size=1 31 0.0238774 malignant (0.2912890 0.7087110) *
## 
## $trees[[172]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.47083290 malignant (0.4418636 0.5581364)  
##   2) Cell.shape=1,2 286 0.08997291 benign (0.5850881 0.4149119) *
##   3) Cell.shape=3,4,5,6,7,8,9,10 193 0.32823830 malignant (0.3993911 0.6006089) *
## 
## $trees[[173]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.48502030 benign (0.5299325 0.4700675)  
##   2) Epith.c.size=2,3,4,5,6,7,8,9,10 448 0.41795740 benign (0.5552909 0.4447091) *
##   3) Epith.c.size=1 31 0.02345393 malignant (0.2707823 0.7292177) *
## 
## $trees[[174]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.46653810 malignant (0.4333745 0.5666255)  
##   2) Normal.nucleoli=2 23 0.01058392 benign (0.7676172 0.2323828) *
##   3) Normal.nucleoli=1,3,4,5,6,7,8,9,10 456 0.42656170 malignant (0.4163830 0.5836170) *
## 
## $trees[[175]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.476593500 benign (0.5467106 0.4532894)  
##   2) Mitoses=1,2 418 0.446458000 benign (0.5609188 0.4390812) *
##   3) Mitoses=3,4,5,6,7,8,10 61 0.004074857 malignant (0.1292984 0.8707016) *
## 
## $trees[[176]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.47709970 malignant (0.4542953 0.5457047)  
##   2) Marg.adhesion=1,2,3,4,5,6 412 0.45980090 malignant (0.4751590 0.5248410) *
##   3) Marg.adhesion=7,8,9,10 67 0.01729882 malignant (0.2096338 0.7903662) *
## 
## $trees[[177]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.50000000 benign (0.5000000 0.5000000)  
##   2) Marg.adhesion=1,2,3,4,5,6 412 0.44309830 benign (0.5209588 0.4790412) *
##   3) Marg.adhesion=7,8,9,10 67 0.01812915 malignant (0.2416226 0.7583774) *
## 
## $trees[[178]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.46684640 malignant (0.4339830 0.5660170)  
##   2) Epith.c.size=7,8,9,10 49 0.02158969 benign (0.6906982 0.3093018) *
##   3) Epith.c.size=1,2,3,4,5,6 430 0.41178710 malignant (0.4134368 0.5865632) *
## 
## $trees[[179]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 4.763232e-01 benign (0.5472476010 0.4527523990)  
##   2) Epith.c.size=1,2,3,4,5,6,7 439 4.516032e-01 benign (0.5604053091 0.4395946909) *
##   3) Epith.c.size=8,9,10 40 2.180598e-05 malignant (0.0009688726 0.9990311274) *
## 
## $trees[[180]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 4.774848e-01 malignant (0.4550606605 0.5449393395)  
##   2) Epith.c.size=1,2,3,4,5,6,7 439 4.774606e-01 malignant (0.4660025410 0.5339974590) *
##   3) Epith.c.size=8,9,10 40 2.414169e-05 malignant (0.0009778258 0.9990221742) *
## 
## $trees[[181]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 5.000000e-01 benign (0.500000000 0.500000000)  
##   2) Epith.c.size=1,2,3,4,5,6,7 439 4.784318e-01 benign (0.511009161 0.488990839) *
##   3) Epith.c.size=8,9,10 40 2.528006e-05 malignant (0.001170729 0.998829271) *
## 
## $trees[[182]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4793492 malignant (0.4587687 0.5412313) *
## 
## $trees[[183]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Cl.thickness=1,2,3,4,5,6,7,8 418 0.4786828 benign (0.5108908 0.4891092) *
##   3) Cl.thickness=9,10 61 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[184]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4795545 malignant (0.4591772 0.5408228) *
## 
## $trees[[185]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Bare.nuclei=1,2,3,4,5,7,8 379 0.4790111 benign (0.5107194 0.4892806) *
##   3) Bare.nuclei=6,9,10 100 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[186]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4798567 malignant (0.4597786 0.5402214)  
##   2) Normal.nucleoli=1,2,3,4,5,6,7,8 432 0.4798567 malignant (0.4700573 0.5299427) *
##   3) Normal.nucleoli=9,10 47 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[187]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Normal.nucleoli=1,2,3,4,5,6,7,8 432 0.4797611 benign (0.5103285 0.4896715) *
##   3) Normal.nucleoli=9,10 47 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[188]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4805485 malignant (0.4611557 0.5388443) *
## 
## $trees[[189]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Cell.shape=1,2,3,4,5,6,7,8 430 0.4797607 benign (0.5103287 0.4896713) *
##   3) Cell.shape=9,10 49 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[190]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4805481 malignant (0.4611549 0.5388451) *
## 
## $trees[[191]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 malignant (0.5000000 0.5000000)  
##   2) Bl.cromatin=1,2,3,4,5,7 437 0.4798265 benign (0.5102944 0.4897056) *
##   3) Bl.cromatin=6,8,9,10 42 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[192]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.48060890 malignant (0.4612761 0.5387239)  
##   2) Bare.nuclei=1,2,3,5,7,8 367 0.45797230 benign (0.4820835 0.5179165) *
##   3) Bare.nuclei=4,6,9,10 112 0.01992353 malignant (0.2308678 0.7691322) *
## 
## $trees[[193]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4620265000 malignant (0.42448862 0.57551138)  
##   2) Cl.thickness=1 99 0.0001857315 benign (0.98786816 0.01213184) *
##   3) Cl.thickness=2,3,4,5,6,7,8,9,10 380 0.4444168000 malignant (0.41510815 0.58489185) *
## 
## $trees[[194]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 4.843556e-01 benign (0.531258115 0.468741885)  
##   2) Epith.c.size=1,2,3,4,5,6,7 439 4.648083e-01 benign (0.541490799 0.458509201) *
##   3) Epith.c.size=8,9,10 40 2.283621e-05 malignant (0.001242177 0.998757823) *
## 
## $trees[[195]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4817618 malignant (0.4635721 0.5364279)  
##   2) Cl.thickness=1,2,3,4,5,6,7,8 418 0.4817618 malignant (0.4733104 0.5266896) *
##   3) Cl.thickness=9,10 61 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[196]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Cl.thickness=1,2,3,4,5,6,7,8 418 0.4808223 benign (0.5097763 0.4902237) *
##   3) Cl.thickness=9,10 61 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[197]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4815307 malignant (0.4631117 0.5368883)  
##   2) Normal.nucleoli=1,2,3,4,5,6,7,8 432 0.4815307 malignant (0.4727798 0.5272202) *
##   3) Normal.nucleoli=9,10 47 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[198]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.5000000 benign (0.5000000 0.5000000)  
##   2) Normal.nucleoli=1,2,3,4,5,6,7,8 432 0.4809555 benign (0.5097071 0.4902929) *
##   3) Normal.nucleoli=9,10 47 0.0000000 malignant (0.0000000 1.0000000) *
## 
## $trees[[199]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.4816543 malignant (0.4633579 0.5366421) *
## 
## $trees[[200]]
## n= 479 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 479 0.500000 benign (0.5000000 0.5000000)  
##   2) Bl.cromatin=1,2,3,4,5,7 437 0.481152 benign (0.5096051 0.4903949) *
##   3) Bl.cromatin=6,8,9,10 42 0.000000 malignant (0.0000000 1.0000000) *
## 
## 
## $weights
##   [1] 2.57171376 1.98562559 1.53214494 1.00646554 1.06984804 0.58019994
##   [7] 0.54512345 0.69174862 0.63907941 0.93493197 0.61633190 0.73738093
##  [13] 0.27416883 0.44621535 0.35442263 0.34045528 0.49082060 0.42428973
##  [19] 0.21698205 0.28573590 0.24136509 0.27664787 0.51548291 0.20395515
##  [25] 0.59280695 0.33524790 0.28585216 0.25451148 0.22565756 0.24769525
##  [31] 0.21890807 0.25537531 0.21519545 0.20224613 0.18362034 0.25376556
##  [37] 0.28877261 0.34724792 0.20579387 0.46003697 0.48403854 0.39373896
##  [43] 0.32694841 0.17265520 0.16643897 0.27691471 0.29552284 0.24188938
##  [49] 0.20470745 0.17842233 0.16262279 0.16328323 0.15093530 0.14175468
##  [55] 0.38589301 0.47117978 0.18349372 0.21686071 0.17116546 0.15764353
##  [61] 0.16427411 0.15178131 0.14105727 0.34749070 0.28151850 0.34715485
##  [67] 0.22652739 0.23132047 0.19201268 0.15830614 0.14667397 0.15103142
##  [73] 0.14040954 0.14115179 0.13183312 0.13129701 0.12319832 0.12921979
##  [79] 0.12134513 0.12706752 0.11946768 0.12403806 0.36468519 0.23019104
##  [85] 0.13853641 0.12986828 0.12193977 0.12969057 0.12178311 0.12741376
##  [91] 0.35999102 0.24907727 0.12762383 0.12448467 0.11718211 0.11558929
##  [97] 0.10926756 0.11573336 0.10939628 0.13887734 0.12764782 0.11299389
## [103] 0.20316297 0.19089730 0.10672826 0.27030309 0.17784385 0.10512612
## [109] 0.18273329 0.12627074 0.11712958 0.10524783 0.27941676 0.17894463
## [115] 0.19117953 0.15983386 0.17713181 0.15256223 0.11325275 0.10717754
## [121] 0.10891407 0.10328424 0.10374646 0.09862604 0.12573994 0.11640063
## [127] 0.10222514 0.09723852 0.09953589 0.09481350 0.09911853 0.18011771
## [133] 0.16796562 0.08258451 0.09346084 0.25396065 0.28618476 0.25190239
## [139] 0.29995944 0.35009897 0.20299337 0.09881186 0.11155498 0.10565597
## [145] 0.18497743 0.15490063 0.10233468 0.09733475 0.10202147 0.09706598
## [151] 0.10148754 0.18103001 0.16841008 0.08411053 0.09419107 0.08993918
## [157] 0.09360870 0.08942051 0.11220890 0.10463551 0.08869717 0.08492836
## [163] 0.08748885 0.08381995 0.08658979 0.08299441 0.08659347 0.08299779
## [169] 0.08700436 0.16189200 0.22740718 0.33012105 0.23543634 0.25275466
## [175] 0.19851814 0.09166521 0.15540217 0.26808715 0.19410728 0.09012188
## [181] 0.08622486 0.08265037 0.08532057 0.08182774 0.08400499 0.08061696
## [187] 0.08099981 0.07784542 0.08100149 0.07784697 0.08073764 0.08847438
## [193] 0.22250313 0.14090795 0.07298508 0.07674852 0.07391090 0.07621487
## [199] 0.07341587 0.07542793
## 
## $votes
##             [,1]     [,2]
##   [1,] 28.509864 18.20407
##   [2,] 28.551731 18.16220
##   [3,] 24.333644 22.38029
##   [4,] 14.045281 32.66865
##   [5,] 29.444796 17.26914
##   [6,] 28.981044 17.73289
##   [7,] 28.027098 18.68684
##   [8,] 22.802721 23.91121
##   [9,] 29.225567 17.48837
##  [10,] 12.903357 33.81058
##  [11,] 23.429685 23.28425
##  [12,] 28.981044 17.73289
##  [13,] 28.509864 18.20407
##  [14,] 28.509864 18.20407
##  [15,] 13.205685 33.50825
##  [16,] 16.298775 30.41516
##  [17,] 29.915976 16.79796
##  [18,] 30.118633 16.59530
##  [19,] 23.101644 23.61229
##  [20,] 28.981044 17.73289
##  [21,] 29.915976 16.79796
##  [22,] 27.824441 18.88949
##  [23,] 15.832284 30.88165
##  [24,] 28.694218 18.01972
##  [25,] 29.915976 16.79796
##  [26,] 29.915976 16.79796
##  [27,] 26.889509 19.82442
##  [28,] 22.627790 24.08614
##  [29,] 19.865676 26.84826
##  [30,] 13.251170 33.46276
##  [31,] 22.287551 24.42638
##  [32,] 12.868022 33.84591
##  [33,] 26.689438 20.02450
##  [34,] 23.011877 23.70206
##  [35,] 17.741234 28.97270
##  [36,] 16.609733 30.10420
##  [37,] 16.745900 29.96803
##  [38,] 17.968661 28.74527
##  [39,] 21.300800 25.41313
##  [40,] 19.644042 27.06989
##  [41,] 20.598892 26.11504
##  [42,] 29.696747 17.01719
##  [43,] 15.653824 31.06011
##  [44,] 22.202099 24.51183
##  [45,] 19.914115 26.79982
##  [46,] 28.509864 18.20407
##  [47,] 16.372514 30.34142
##  [48,] 19.154118 27.55982
##  [49,] 28.896681 17.81725
##  [50,] 14.525556 32.18838
##  [51,] 29.953069 16.76086
##  [52,] 25.658752 21.05518
##  [53,] 28.551731 18.16220
##  [54,] 27.824441 18.88949
##  [55,] 28.230466 18.48347
##  [56,] 28.509864 18.20407
##  [57,] 29.022911 17.69102
##  [58,] 15.803235 30.91070
##  [59,] 22.378098 24.33583
##  [60,] 28.509864 18.20407
##  [61,] 29.165398 17.54854
##  [62,] 30.118633 16.59530
##  [63,] 30.595130 16.11880
##  [64,] 30.118633 16.59530
##  [65,] 30.595130 16.11880
##  [66,] 28.509864 18.20407
##  [67,]  8.061884 38.65205
##  [68,] 17.081163 29.63277
##  [69,] 12.858179 33.85575
##  [70,] 21.966359 24.74757
##  [71,] 17.825996 28.88794
##  [72,] 31.105295 15.60864
##  [73,] 17.599925 29.11401
##  [74,] 25.879061 20.83487
##  [75,] 12.535459 34.17847
##  [76,] 25.980017 20.73392
##  [77,] 30.672998 16.04093
##  [78,] 26.240346 20.47359
##  [79,] 28.551731 18.16220
##  [80,] 27.616799 19.09713
##  [81,]  8.766268 37.94766
##  [82,] 30.589812 16.12412
##  [83,] 13.009862 33.70407
##  [84,] 18.509806 28.20413
##  [85,] 30.826115 15.88782
##  [86,] 28.058116 18.65582
##  [87,] 10.717221 35.99671
##  [88,] 32.468064 14.24587
##  [89,] 28.132282 18.58165
##  [90,] 28.981044 17.73289
##  [91,] 31.345708 15.36822
##  [92,] 31.345708 15.36822
##  [93,] 31.345708 15.36822
##  [94,] 17.003465 29.71047
##  [95,] 30.672998 16.04093
##  [96,] 29.915976 16.79796
##  [97,] 23.164175 23.54976
##  [98,] 15.972596 30.74134
##  [99,] 28.027098 18.68684
## [100,] 18.697112 28.01682
## [101,] 29.517711 17.19622
## [102,] 32.019545 14.69439
## [103,] 17.734541 28.97939
## [104,] 30.589812 16.12412
## [105,] 29.444796 17.26914
## [106,]  8.185466 38.52847
## [107,] 15.672662 31.04127
## [108,] 31.061953 15.65198
## [109,] 29.444796 17.26914
## [110,] 25.894719 20.81921
## [111,] 30.168887 16.54505
## [112,] 10.209193 36.50474
## [113,] 29.444796 17.26914
## [114,] 29.177431 17.53650
## [115,] 31.345708 15.36822
## [116,] 30.118633 16.59530
## [117,] 30.589812 16.12412
## [118,] 16.518187 30.19575
## [119,] 29.444796 17.26914
## [120,] 14.195159 32.51877
## [121,] 28.509864 18.20407
## [122,] 28.027098 18.68684
## [123,] 32.019545 14.69439
## [124,] 18.253542 28.46039
## [125,] 16.760946 29.95299
## [126,] 12.557863 34.15607
## [127,] 29.195705 17.51823
## [128,] 28.981044 17.73289
## [129,] 30.118633 16.59530
## [130,] 29.444796 17.26914
## [131,] 23.328995 23.38494
## [132,] 26.689438 20.02450
## [133,] 32.019545 14.69439
## [134,] 29.915976 16.79796
## [135,] 14.125900 32.58803
## [136,] 30.118633 16.59530
## [137,] 30.118633 16.59530
## [138,]  8.916997 37.79694
## [139,] 15.971691 30.74224
## [140,] 26.418329 20.29560
## [141,]  7.229462 39.48447
## [142,] 30.118633 16.59530
## [143,]  5.711058 41.00287
## [144,]  8.093476 38.62046
## [145,] 17.218567 29.49537
## [146,] 30.118633 16.59530
## [147,] 14.830181 31.88375
## [148,] 29.368054 17.34588
## [149,] 23.722537 22.99140
## [150,] 20.913129 25.80080
## [151,] 12.525453 34.18848
## [152,] 30.589812 16.12412
## [153,] 11.271818 35.44212
## [154,] 22.173218 24.54072
## [155,]  7.121582 39.59235
## [156,] 20.503296 26.21064
## [157,] 23.779784 22.93415
## [158,] 26.181334 20.53260
## [159,] 13.748357 32.96558
## [160,] 14.735220 31.97871
## [161,] 15.019966 31.69397
## [162,] 25.860107 20.85383
## [163,] 28.232390 18.48154
## [164,] 26.866221 19.84771
## [165,] 14.862837 31.85110
## [166,] 19.636552 27.07738
## [167,] 27.818116 18.89582
## [168,] 32.019545 14.69439
## [169,] 13.251170 33.46276
## [170,] 15.653824 31.06011
## [171,] 29.915976 16.79796
## [172,] 29.444796 17.26914
## [173,] 23.046373 23.66756
## [174,]  9.595143 37.11879
## [175,] 17.605332 29.10860
## [176,] 11.984790 34.72914
## [177,] 16.460598 30.25334
## [178,] 30.425209 16.28872
## [179,] 20.210501 26.50343
## [180,] 17.062678 29.65126
## [181,] 30.118633 16.59530
## [182,] 20.210501 26.50343
## [183,] 31.996884 14.71705
## [184,] 29.915976 16.79796
## [185,] 30.589812 16.12412
## [186,] 30.118633 16.59530
## [187,] 16.285414 30.42852
## [188,] 29.444796 17.26914
## [189,] 16.089650 30.62428
## [190,] 16.056729 30.65720
## [191,] 16.998365 29.71557
## [192,] 10.194118 36.51982
## [193,]  3.995581 42.71835
## [194,] 11.619842 35.09409
## [195,] 30.118633 16.59530
## [196,] 15.247179 31.46675
## [197,] 16.753463 29.96047
## [198,] 24.303497 22.41044
## [199,] 29.976877 16.73706
## [200,] 19.891996 26.82194
## [201,] 16.442072 30.27186
## [202,] 30.118633 16.59530
## [203,]  4.249541 42.46439
## [204,] 30.118633 16.59530
## [205,] 16.080096 30.63384
## [206,]  9.664627 37.04931
## [207,] 30.118633 16.59530
## [208,] 27.558554 19.15538
## [209,] 29.915976 16.79796
## [210,] 32.019545 14.69439
## [211,] 20.117623 26.59631
## [212,] 28.498277 18.21566
## [213,] 19.537092 27.17684
## [214,] 16.217448 30.49649
## [215,] 30.118633 16.59530
## [216,] 23.121098 23.59284
## [217,] 16.099511 30.61442
## [218,] 29.444796 17.26914
## [219,] 16.215731 30.49820
## [220,] 30.118633 16.59530
## [221,] 30.431459 16.28247
## [222,] 19.033474 27.68046
## [223,] 17.698431 29.01550
## [224,] 14.731696 31.98224
## [225,] 28.509864 18.20407
## [226,] 30.782554 15.93138
## [227,] 18.467244 28.24669
## [228,] 17.584414 29.12952
## [229,] 32.019545 14.69439
## [230,] 30.118633 16.59530
## [231,] 28.498277 18.21566
## [232,] 18.033345 28.68059
## [233,] 15.574189 31.13974
## [234,] 30.118633 16.59530
## [235,] 32.019545 14.69439
## [236,] 10.848982 35.86495
## [237,] 30.443492 16.27044
## [238,] 23.195366 23.51857
## [239,] 23.007136 23.70680
## [240,] 28.590350 18.12358
## [241,] 23.316405 23.39753
## [242,] 10.872521 35.84141
## [243,] 30.589812 16.12412
## [244,] 22.247993 24.46594
## [245,] 10.137133 36.57680
## [246,] 15.644191 31.06974
## [247,]  6.469931 40.24400
## [248,] 12.711451 34.00248
## [249,] 29.022911 17.69102
## [250,] 24.128943 22.58499
## [251,] 29.444796 17.26914
## [252,] 29.915976 16.79796
## [253,]  7.723567 38.99037
## [254,] 10.620167 36.09377
## [255,] 27.575349 19.13858
## [256,] 31.096617 15.61732
## [257,] 28.981044 17.73289
## [258,] 28.230466 18.48347
## [259,] 29.915976 16.79796
## [260,] 32.019545 14.69439
## [261,] 30.589812 16.12412
## [262,] 25.301407 21.41253
## [263,] 32.019545 14.69439
## [264,] 13.839715 32.87422
## [265,] 31.345708 15.36822
## [266,] 28.185858 18.52808
## [267,] 17.421723 29.29221
## [268,] 24.264644 22.44929
## [269,] 32.468064 14.24587
## [270,] 30.640067 16.07387
## [271,] 29.839234 16.87470
## [272,] 12.525453 34.18848
## [273,] 29.915976 16.79796
## [274,] 29.928010 16.78592
## [275,] 29.666884 17.04705
## [276,] 30.410776 16.30316
## [277,] 32.468064 14.24587
## [278,] 13.396099 33.31783
## [279,] 31.345708 15.36822
## [280,] 32.751206 13.96273
## [281,] 30.589812 16.12412
## [282,] 28.981044 17.73289
## [283,] 25.229439 21.48449
## [284,] 29.915976 16.79796
## [285,] 30.589812 16.12412
## [286,] 12.154658 34.55928
## [287,] 24.159591 22.55434
## [288,] 19.130481 27.58345
## [289,] 30.589812 16.12412
## [290,] 28.773995 17.93994
## [291,] 26.731305 19.98263
## [292,] 25.530705 21.18323
## [293,] 28.058116 18.65582
## [294,]  5.385552 41.32838
## [295,] 30.589812 16.12412
## [296,] 29.915976 16.79796
## [297,] 30.570187 16.14375
## [298,] 29.241526 17.47241
## [299,] 31.533132 15.18080
## [300,] 29.525282 17.18865
## [301,] 23.711483 23.00245
## [302,] 13.590905 33.12303
## [303,] 21.125606 25.58833
## [304,] 30.410776 16.30316
## [305,] 30.410776 16.30316
## [306,] 25.670744 21.04319
## [307,] 29.306053 17.40788
## [308,] 31.126479 15.58745
## [309,] 27.160618 19.55332
## [310,] 31.345708 15.36822
## [311,] 32.019545 14.69439
## [312,] 30.410776 16.30316
## [313,] 29.928010 16.78592
## [314,] 11.059473 35.65446
## [315,] 25.069083 21.64485
## [316,] 29.525282 17.18865
## [317,] 11.050282 35.66365
## [318,] 29.999162 16.71477
## [319,] 28.590350 18.12358
## [320,] 32.962865 13.75107
## [321,] 30.410776 16.30316
## [322,] 16.499751 30.21418
## [323,] 30.410776 16.30316
## [324,] 30.589812 16.12412
## [325,] 28.319241 18.39469
## [326,] 30.410776 16.30316
## [327,] 30.410776 16.30316
## [328,] 31.345708 15.36822
## [329,] 30.410776 16.30316
## [330,] 30.410776 16.30316
## [331,] 16.469692 30.24424
## [332,] 28.319241 18.39469
## [333,]  7.395799 39.31813
## [334,] 14.311944 32.40199
## [335,] 27.214518 19.49942
## [336,] 27.824441 18.88949
## [337,]  6.395578 40.31835
## [338,] 22.070589 24.64334
## [339,] 23.235763 23.47817
## [340,] 32.019545 14.69439
## [341,] 16.255381 30.45855
## [342,] 26.889509 19.82442
## [343,] 12.259250 34.45468
## [344,] 24.078042 22.63589
## [345,] 27.824441 18.88949
## [346,] 28.981044 17.73289
## [347,] 28.981044 17.73289
## [348,] 28.509864 18.20407
## [349,] 28.230466 18.48347
## [350,] 28.509864 18.20407
## [351,] 32.019545 14.69439
## [352,] 16.887211 29.82672
## [353,] 29.099938 17.61400
## [354,] 30.410776 16.30316
## [355,] 31.345708 15.36822
## [356,] 28.981044 17.73289
## [357,] 28.498277 18.21566
## [358,] 16.952086 29.76185
## [359,] 32.019545 14.69439
## [360,] 15.190300 31.52363
## [361,] 30.595130 16.11880
## [362,] 30.410776 16.30316
## [363,] 28.509864 18.20407
## [364,] 28.737270 17.97666
## [365,] 26.889509 19.82442
## [366,] 29.915976 16.79796
## [367,] 28.445126 18.26881
## [368,] 28.509864 18.20407
## [369,] 28.509864 18.20407
## [370,] 28.981044 17.73289
## [371,] 28.981044 17.73289
## [372,] 28.087979 18.62595
## [373,] 31.345708 15.36822
## [374,] 27.839063 18.87487
## [375,] 28.981044 17.73289
## [376,] 28.242470 18.47146
## [377,] 29.254173 17.45976
## [378,] 29.254173 17.45976
## [379,] 29.915976 16.79796
## [380,] 30.118633 16.59530
## [381,] 31.463525 15.25041
## [382,] 27.939220 18.77471
## [383,] 29.915976 16.79796
## [384,] 28.509864 18.20407
## [385,] 28.509864 18.20407
## [386,] 29.915976 16.79796
## [387,] 11.059473 35.65446
## [388,] 19.592248 27.12168
## [389,] 15.650942 31.06299
## [390,] 16.870380 29.84355
## [391,] 12.011505 34.70243
## [392,] 29.915976 16.79796
## [393,] 21.536913 25.17702
## [394,] 28.981044 17.73289
## [395,] 30.589812 16.12412
## [396,] 30.118633 16.59530
## [397,] 16.255381 30.45855
## [398,] 13.739378 32.97456
## [399,] 31.345708 15.36822
## [400,] 28.590350 18.12358
## [401,] 32.019545 14.69439
## [402,] 17.595080 29.11885
## [403,] 30.410776 16.30316
## [404,] 21.692060 25.02187
## [405,] 18.448905 28.26503
## [406,] 30.410776 16.30316
## [407,] 18.205721 28.50821
## [408,] 28.981044 17.73289
## [409,] 29.915976 16.79796
## [410,] 27.682498 19.03144
## [411,] 29.915976 16.79796
## [412,] 28.498277 18.21566
## [413,] 28.981044 17.73289
## [414,] 18.756253 27.95768
## [415,] 15.852800 30.86113
## [416,] 29.660198 17.05374
## [417,] 32.019545 14.69439
## [418,] 30.410776 16.30316
## [419,] 12.709482 34.00445
## [420,] 17.112624 29.60131
## [421,] 27.344262 19.36967
## [422,] 27.824441 18.88949
## [423,] 28.058116 18.65582
## [424,] 28.981044 17.73289
## [425,] 28.981044 17.73289
## [426,] 29.915976 16.79796
## [427,] 26.376620 20.33731
## [428,] 32.019545 14.69439
## [429,] 26.138931 20.57500
## [430,] 27.503174 19.21076
## [431,] 31.345708 15.36822
## [432,] 28.981044 17.73289
## [433,] 32.019545 14.69439
## [434,] 19.408077 27.30586
## [435,] 30.708965 16.00497
## [436,] 12.154203 34.55973
## [437,] 25.630809 21.08312
## [438,] 30.410776 16.30316
## [439,] 28.590350 18.12358
## [440,] 29.915976 16.79796
## [441,] 32.019545 14.69439
## [442,] 31.345708 15.36822
## [443,] 30.199118 16.51481
## [444,] 11.323305 35.39063
## [445,] 29.915976 16.79796
## [446,] 27.675524 19.03841
## [447,] 28.769386 17.94455
## [448,] 28.981044 17.73289
## [449,] 29.444796 17.26914
## [450,] 29.915976 16.79796
## [451,] 28.981044 17.73289
## [452,] 15.848135 30.86580
## [453,] 30.589812 16.12412
## [454,] 28.509864 18.20407
## [455,] 29.666884 17.04705
## [456,] 28.095550 18.61838
## [457,] 29.660198 17.05374
## [458,] 29.444796 17.26914
## [459,] 15.706536 31.00740
## [460,] 28.694218 18.01972
## [461,] 29.444796 17.26914
## [462,] 27.839063 18.87487
## [463,] 30.589812 16.12412
## [464,] 29.774033 16.93990
## [465,] 30.589812 16.12412
## [466,] 32.019545 14.69439
## [467,] 31.345708 15.36822
## [468,]  4.189336 42.52460
## [469,]  9.420614 37.29332
## [470,] 32.019545 14.69439
## [471,] 32.019545 14.69439
## [472,] 30.431459 16.28247
## [473,] 30.410776 16.30316
## [474,] 29.755638 16.95830
## [475,] 19.678619 27.03531
## [476,] 31.345708 15.36822
## [477,] 29.915976 16.79796
## [478,] 16.910498 29.80343
## [479,] 21.635751 25.07818
## 
## $prob
##              [,1]      [,2]
##   [1,] 0.61030751 0.3896925
##   [2,] 0.61120374 0.3887963
##   [3,] 0.52090762 0.4790924
##   [4,] 0.30066578 0.6993342
##   [5,] 0.63032150 0.3696785
##   [6,] 0.62039401 0.3796060
##   [7,] 0.59997298 0.4000270
##   [8,] 0.48813533 0.5118647
##   [9,] 0.62562848 0.3743715
##  [10,] 0.27622074 0.7237793
##  [11,] 0.50155668 0.4984433
##  [12,] 0.62039401 0.3796060
##  [13,] 0.61030751 0.3896925
##  [14,] 0.61030751 0.3896925
##  [15,] 0.28269265 0.7173074
##  [16,] 0.34890607 0.6510939
##  [17,] 0.64040799 0.3595920
##  [18,] 0.64474624 0.3552538
##  [19,] 0.49453433 0.5054657
##  [20,] 0.62039401 0.3796060
##  [21,] 0.64040799 0.3595920
##  [22,] 0.59563473 0.4043653
##  [23,] 0.33891996 0.6610800
##  [24,] 0.61425395 0.3857460
##  [25,] 0.64040799 0.3595920
##  [26,] 0.64040799 0.3595920
##  [27,] 0.57562075 0.4243793
##  [28,] 0.48439060 0.5156094
##  [29,] 0.42526233 0.5747377
##  [30,] 0.28366633 0.7163337
##  [31,] 0.47710714 0.5228929
##  [32,] 0.27546431 0.7245357
##  [33,] 0.57133785 0.4286622
##  [34,] 0.49261270 0.5073873
##  [35,] 0.37978463 0.6202154
##  [36,] 0.35556271 0.6444373
##  [37,] 0.35847764 0.6415224
##  [38,] 0.38465314 0.6153469
##  [39,] 0.45598387 0.5440161
##  [40,] 0.42051783 0.5794822
##  [41,] 0.44095820 0.5590418
##  [42,] 0.63571497 0.3642850
##  [43,] 0.33509968 0.6649003
##  [44,] 0.47527788 0.5247221
##  [45,] 0.42629926 0.5737007
##  [46,] 0.61030751 0.3896925
##  [47,] 0.35048460 0.6495154
##  [48,] 0.41003008 0.5899699
##  [49,] 0.61858804 0.3814120
##  [50,] 0.31094698 0.6890530
##  [51,] 0.64120204 0.3587980
##  [52,] 0.54927407 0.4507259
##  [53,] 0.61120374 0.3887963
##  [54,] 0.59563473 0.4043653
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##  [57,] 0.62129024 0.3787098
##  [58,] 0.33829811 0.6617019
##  [59,] 0.47904548 0.5209545
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##  [61,] 0.62434045 0.3756596
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##  [65,] 0.65494657 0.3450534
##  [66,] 0.61030751 0.3896925
##  [67,] 0.17257987 0.8274201
##  [68,] 0.36565457 0.6343454
##  [69,] 0.27525361 0.7247464
##  [70,] 0.47023142 0.5297686
##  [71,] 0.38159914 0.6184009
##  [72,] 0.66586761 0.3341324
##  [73,] 0.37675966 0.6232403
##  [74,] 0.55399021 0.4460098
##  [75,] 0.26834519 0.7316548
##  [76,] 0.55615135 0.4438486
##  [77,] 0.65661348 0.3433865
##  [78,] 0.56172419 0.4382758
##  [79,] 0.61120374 0.3887963
##  [80,] 0.59118976 0.4088102
##  [81,] 0.18765853 0.8123415
##  [82,] 0.65483273 0.3451673
##  [83,] 0.27850069 0.7214993
##  [84,] 0.39623737 0.6037626
##  [85,] 0.65989123 0.3401088
##  [86,] 0.60063699 0.3993630
##  [87,] 0.22942237 0.7705776
##  [88,] 0.69504026 0.3049597
##  [89,] 0.60222464 0.3977754
##  [90,] 0.62039401 0.3796060
##  [91,] 0.67101411 0.3289859
##  [92,] 0.67101411 0.3289859
##  [93,] 0.67101411 0.3289859
##  [94,] 0.36399130 0.6360087
##  [95,] 0.65661348 0.3433865
##  [96,] 0.64040799 0.3595920
##  [97,] 0.49587293 0.5041271
##  [98,] 0.34192361 0.6580764
##  [99,] 0.59997298 0.4000270
## [100,] 0.40024700 0.5997530
## [101,] 0.63188237 0.3681176
## [102,] 0.68543885 0.3145612
## [103,] 0.37964137 0.6203586
## [104,] 0.65483273 0.3451673
## [105,] 0.63032150 0.3696785
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## [107,] 0.33550295 0.6644971
## [108,] 0.66493978 0.3350602
## [109,] 0.63032150 0.3696785
## [110,] 0.55432539 0.4456746
## [111,] 0.64582203 0.3541780
## [112,] 0.21854706 0.7814529
## [113,] 0.63032150 0.3696785
## [114,] 0.62459805 0.3754020
## [115,] 0.67101411 0.3289859
## [116,] 0.64474624 0.3552538
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## [118,] 0.35360300 0.6463970
## [119,] 0.63032150 0.3696785
## [120,] 0.30387421 0.6961258
## [121,] 0.61030751 0.3896925
## [122,] 0.59997298 0.4000270
## [123,] 0.68543885 0.3145612
## [124,] 0.39075156 0.6092484
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## [126,] 0.26882478 0.7311752
## [127,] 0.62498922 0.3750108
## [128,] 0.62039401 0.3796060
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## [130,] 0.63032150 0.3696785
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## [138,] 0.19088516 0.8091148
## [139,] 0.34190422 0.6580958
## [140,] 0.56553425 0.4344657
## [141,] 0.15476030 0.8452397
## [142,] 0.64474624 0.3552538
## [143,] 0.12225599 0.8777440
## [144,] 0.17325615 0.8267438
## [145,] 0.36859596 0.6314040
## [146,] 0.64474624 0.3552538
## [147,] 0.31746805 0.6825320
## [148,] 0.62867869 0.3713213
## [149,] 0.50782572 0.4921743
## [150,] 0.44768504 0.5523150
## [151,] 0.26813099 0.7318690
## [152,] 0.65483273 0.3451673
## [153,] 0.24129456 0.7587054
## [154,] 0.47465963 0.5253404
## [155,] 0.15245092 0.8475491
## [156,] 0.43891180 0.5610882
## [157,] 0.50905121 0.4909488
## [158,] 0.56046092 0.4395391
## [159,] 0.29430957 0.7056904
## [160,] 0.31543523 0.6845648
## [161,] 0.32153074 0.6784693
## [162,] 0.55358446 0.4464155
## [163,] 0.60436765 0.3956324
## [164,] 0.57512221 0.4248778
## [165,] 0.31816711 0.6818329
## [166,] 0.42035749 0.5796425
## [167,] 0.59549933 0.4045007
## [168,] 0.68543885 0.3145612
## [169,] 0.28366633 0.7163337
## [170,] 0.33509968 0.6649003
## [171,] 0.64040799 0.3595920
## [172,] 0.63032150 0.3696785
## [173,] 0.49335117 0.5066488
## [174,] 0.20540217 0.7945978
## [175,] 0.37687540 0.6231246
## [176,] 0.25655707 0.7434429
## [177,] 0.35237020 0.6476298
## [178,] 0.65130909 0.3486909
## [179,] 0.43264397 0.5673560
## [180,] 0.36525886 0.6347411
## [181,] 0.64474624 0.3552538
## [182,] 0.43264397 0.5673560
## [183,] 0.68495377 0.3150462
## [184,] 0.64040799 0.3595920
## [185,] 0.65483273 0.3451673
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## [188,] 0.63032150 0.3696785
## [189,] 0.34442936 0.6555706
## [190,] 0.34372462 0.6562754
## [191,] 0.36388211 0.6361179
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## [193,] 0.08553295 0.9144670
## [194,] 0.24874467 0.7512553
## [195,] 0.64474624 0.3552538
## [196,] 0.32639467 0.6736053
## [197,] 0.35863953 0.6413605
## [198,] 0.52026227 0.4797377
## [199,] 0.64171170 0.3582883
## [200,] 0.42582575 0.5741742
## [201,] 0.35197362 0.6480264
## [202,] 0.64474624 0.3552538
## [203,] 0.09096946 0.9090305
## [204,] 0.64474624 0.3552538
## [205,] 0.34422483 0.6557752
## [206,] 0.20688960 0.7931104
## [207,] 0.64474624 0.3552538
## [208,] 0.58994291 0.4100571
## [209,] 0.64040799 0.3595920
## [210,] 0.68543885 0.3145612
## [211,] 0.43065572 0.5693443
## [212,] 0.61005947 0.3899405
## [213,] 0.41822836 0.5817716
## [214,] 0.34716512 0.6528349
## [215,] 0.64474624 0.3552538
## [216,] 0.49495078 0.5050492
## [217,] 0.34464046 0.6553595
## [218,] 0.63032150 0.3696785
## [219,] 0.34712836 0.6528716
## [220,] 0.64474624 0.3552538
## [221,] 0.65144288 0.3485571
## [222,] 0.40744747 0.5925525
## [223,] 0.37886835 0.6211317
## [224,] 0.31535978 0.6846402
## [225,] 0.61030751 0.3896925
## [226,] 0.65895873 0.3410413
## [227,] 0.39532624 0.6046738
## [228,] 0.37642760 0.6235724
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## [230,] 0.64474624 0.3552538
## [231,] 0.61005947 0.3899405
## [232,] 0.38603781 0.6139622
## [233,] 0.33339494 0.6666051
## [234,] 0.64474624 0.3552538
## [235,] 0.68543885 0.3145612
## [236,] 0.23224296 0.7677570
## [237,] 0.65170048 0.3482995
## [238,] 0.49654064 0.5034594
## [239,] 0.49251122 0.5074888
## [240,] 0.61203046 0.3879695
## [241,] 0.49913171 0.5008683
## [242,] 0.23274684 0.7672532
## [243,] 0.65483273 0.3451673
## [244,] 0.47626034 0.5237397
## [245,] 0.21700449 0.7829955
## [246,] 0.33489348 0.6651065
## [247,] 0.13850110 0.8614989
## [248,] 0.27211262 0.7278874
## [249,] 0.62129024 0.3787098
## [250,] 0.51652562 0.4834744
## [251,] 0.63032150 0.3696785
## [252,] 0.64040799 0.3595920
## [253,] 0.16533754 0.8346625
## [254,] 0.22734474 0.7726553
## [255,] 0.59030245 0.4096975
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## [262,] 0.54162443 0.4583756
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## [265,] 0.67101411 0.3289859
## [266,] 0.60337155 0.3966285
## [267,] 0.37294490 0.6270551
## [268,] 0.51943056 0.4805694
## [269,] 0.69504026 0.3049597
## [270,] 0.65590852 0.3440915
## [271,] 0.63876518 0.3612348
## [272,] 0.26813099 0.7318690
## [273,] 0.64040799 0.3595920
## [274,] 0.64066559 0.3593344
## [275,] 0.63507571 0.3649243
## [276,] 0.65100013 0.3489999
## [277,] 0.69504026 0.3049597
## [278,] 0.28676881 0.7132312
## [279,] 0.67101411 0.3289859
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## [282,] 0.62039401 0.3796060
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## [284,] 0.64040799 0.3595920
## [285,] 0.65483273 0.3451673
## [286,] 0.26019341 0.7398066
## [287,] 0.51718169 0.4828183
## [288,] 0.40952408 0.5904759
## [289,] 0.65483273 0.3451673
## [290,] 0.61596172 0.3840383
## [291,] 0.57223408 0.4277659
## [292,] 0.54653299 0.4534670
## [293,] 0.60063699 0.3993630
## [294,] 0.11528792 0.8847121
## [295,] 0.65483273 0.3451673
## [296,] 0.64040799 0.3595920
## [297,] 0.65441261 0.3455874
## [298,] 0.62597012 0.3740299
## [299,] 0.67502628 0.3249737
## [300,] 0.63204445 0.3679556
## [301,] 0.50758910 0.4924109
## [302,] 0.29093901 0.7090610
## [303,] 0.45223352 0.5477665
## [304,] 0.65100013 0.3489999
## [305,] 0.65100013 0.3489999
## [306,] 0.54953077 0.4504692
## [307,] 0.62735143 0.3726486
## [308,] 0.66632109 0.3336789
## [309,] 0.58142434 0.4185757
## [310,] 0.67101411 0.3289859
## [311,] 0.68543885 0.3145612
## [312,] 0.65100013 0.3489999
## [313,] 0.64066559 0.3593344
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## [316,] 0.63204445 0.3679556
## [317,] 0.23655217 0.7634478
## [318,] 0.64218875 0.3578113
## [319,] 0.61203046 0.3879695
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## [331,] 0.35256487 0.6474351
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## [333,] 0.15832106 0.8416789
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## [340,] 0.68543885 0.3145612
## [341,] 0.34797714 0.6520229
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## [345,] 0.59563473 0.4043653
## [346,] 0.62039401 0.3796060
## [347,] 0.62039401 0.3796060
## [348,] 0.61030751 0.3896925
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## [350,] 0.61030751 0.3896925
## [351,] 0.68543885 0.3145612
## [352,] 0.36150265 0.6384973
## [353,] 0.62293915 0.3770608
## [354,] 0.65100013 0.3489999
## [355,] 0.67101411 0.3289859
## [356,] 0.62039401 0.3796060
## [357,] 0.61005947 0.3899405
## [358,] 0.36289143 0.6371086
## [359,] 0.68543885 0.3145612
## [360,] 0.32517708 0.6748229
## [361,] 0.65494657 0.3450534
## [362,] 0.65100013 0.3489999
## [363,] 0.61030751 0.3896925
## [364,] 0.61517556 0.3848244
## [365,] 0.57562075 0.4243793
## [366,] 0.64040799 0.3595920
## [367,] 0.60892167 0.3910783
## [368,] 0.61030751 0.3896925
## [369,] 0.61030751 0.3896925
## [370,] 0.62039401 0.3796060
## [371,] 0.62039401 0.3796060
## [372,] 0.60127625 0.3987237
## [373,] 0.67101411 0.3289859
## [374,] 0.59594773 0.4040523
## [375,] 0.62039401 0.3796060
## [376,] 0.60458343 0.3954166
## [377,] 0.62624085 0.3737591
## [378,] 0.62624085 0.3737591
## [379,] 0.64040799 0.3595920
## [380,] 0.64474624 0.3552538
## [381,] 0.67353621 0.3264638
## [382,] 0.59809180 0.4019082
## [383,] 0.64040799 0.3595920
## [384,] 0.61030751 0.3896925
## [385,] 0.61030751 0.3896925
## [386,] 0.64040799 0.3595920
## [387,] 0.23674892 0.7632511
## [388,] 0.41940910 0.5805909
## [389,] 0.33503798 0.6649620
## [390,] 0.36114235 0.6388576
## [391,] 0.25712895 0.7428710
## [392,] 0.64040799 0.3595920
## [393,] 0.46103831 0.5389617
## [394,] 0.62039401 0.3796060
## [395,] 0.65483273 0.3451673
## [396,] 0.64474624 0.3552538
## [397,] 0.34797714 0.6520229
## [398,] 0.29411734 0.7058827
## [399,] 0.67101411 0.3289859
## [400,] 0.61203046 0.3879695
## [401,] 0.68543885 0.3145612
## [402,] 0.37665593 0.6233441
## [403,] 0.65100013 0.3489999
## [404,] 0.46435953 0.5356405
## [405,] 0.39493366 0.6050663
## [406,] 0.65100013 0.3489999
## [407,] 0.38972786 0.6102721
## [408,] 0.62039401 0.3796060
## [409,] 0.64040799 0.3595920
## [410,] 0.59259618 0.4074038
## [411,] 0.64040799 0.3595920
## [412,] 0.61005947 0.3899405
## [413,] 0.62039401 0.3796060
## [414,] 0.40151304 0.5984870
## [415,] 0.33935913 0.6606409
## [416,] 0.63493258 0.3650674
## [417,] 0.68543885 0.3145612
## [418,] 0.65100013 0.3489999
## [419,] 0.27207047 0.7279295
## [420,] 0.36632806 0.6336719
## [421,] 0.58535560 0.4146444
## [422,] 0.59563473 0.4043653
## [423,] 0.60063699 0.3993630
## [424,] 0.62039401 0.3796060
## [425,] 0.62039401 0.3796060
## [426,] 0.64040799 0.3595920
## [427,] 0.56464138 0.4353586
## [428,] 0.68543885 0.3145612
## [429,] 0.55955320 0.4404468
## [430,] 0.58875740 0.4112426
## [431,] 0.67101411 0.3289859
## [432,] 0.62039401 0.3796060
## [433,] 0.68543885 0.3145612
## [434,] 0.41546656 0.5845334
## [435,] 0.65738342 0.3426166
## [436,] 0.26018368 0.7398163
## [437,] 0.54867590 0.4513241
## [438,] 0.65100013 0.3489999
## [439,] 0.61203046 0.3879695
## [440,] 0.64040799 0.3595920
## [441,] 0.68543885 0.3145612
## [442,] 0.67101411 0.3289859
## [443,] 0.64646919 0.3535308
## [444,] 0.24239674 0.7576033
## [445,] 0.64040799 0.3595920
## [446,] 0.59244687 0.4075531
## [447,] 0.61586307 0.3841369
## [448,] 0.62039401 0.3796060
## [449,] 0.63032150 0.3696785
## [450,] 0.64040799 0.3595920
## [451,] 0.62039401 0.3796060
## [452,] 0.33925928 0.6607407
## [453,] 0.65483273 0.3451673
## [454,] 0.61030751 0.3896925
## [455,] 0.63507571 0.3649243
## [456,] 0.60143833 0.3985617
## [457,] 0.63493258 0.3650674
## [458,] 0.63032150 0.3696785
## [459,] 0.33622809 0.6637719
## [460,] 0.61425395 0.3857460
## [461,] 0.63032150 0.3696785
## [462,] 0.59594773 0.4040523
## [463,] 0.65483273 0.3451673
## [464,] 0.63736943 0.3626306
## [465,] 0.65483273 0.3451673
## [466,] 0.68543885 0.3145612
## [467,] 0.67101411 0.3289859
## [468,] 0.08968065 0.9103194
## [469,] 0.20166605 0.7983340
## [470,] 0.68543885 0.3145612
## [471,] 0.68543885 0.3145612
## [472,] 0.65144288 0.3485571
## [473,] 0.65100013 0.3489999
## [474,] 0.63697565 0.3630243
## [475,] 0.42125803 0.5787420
## [476,] 0.67101411 0.3289859
## [477,] 0.64040799 0.3595920
## [478,] 0.36200117 0.6379988
## [479,] 0.46315412 0.5368459
## 
## $class
##   [1] "benign"    "benign"    "benign"    "malignant" "benign"    "benign"   
##   [7] "benign"    "malignant" "benign"    "malignant" "benign"    "benign"   
##  [13] "benign"    "benign"    "malignant" "malignant" "benign"    "benign"   
##  [19] "malignant" "benign"    "benign"    "benign"    "malignant" "benign"   
##  [25] "benign"    "benign"    "benign"    "malignant" "malignant" "malignant"
##  [31] "malignant" "malignant" "benign"    "malignant" "malignant" "malignant"
##  [37] "malignant" "malignant" "malignant" "malignant" "malignant" "benign"   
##  [43] "malignant" "malignant" "malignant" "benign"    "malignant" "malignant"
##  [49] "benign"    "malignant" "benign"    "benign"    "benign"    "benign"   
##  [55] "benign"    "benign"    "benign"    "malignant" "malignant" "benign"   
##  [61] "benign"    "benign"    "benign"    "benign"    "benign"    "benign"   
##  [67] "malignant" "malignant" "malignant" "malignant" "malignant" "benign"   
##  [73] "malignant" "benign"    "malignant" "benign"    "benign"    "benign"   
##  [79] "benign"    "benign"    "malignant" "benign"    "malignant" "malignant"
##  [85] "benign"    "benign"    "malignant" "benign"    "benign"    "benign"   
##  [91] "benign"    "benign"    "benign"    "malignant" "benign"    "benign"   
##  [97] "malignant" "malignant" "benign"    "malignant" "benign"    "benign"   
## [103] "malignant" "benign"    "benign"    "malignant" "malignant" "benign"   
## [109] "benign"    "benign"    "benign"    "malignant" "benign"    "benign"   
## [115] "benign"    "benign"    "benign"    "malignant" "benign"    "malignant"
## [121] "benign"    "benign"    "benign"    "malignant" "malignant" "malignant"
## [127] "benign"    "benign"    "benign"    "benign"    "malignant" "benign"   
## [133] "benign"    "benign"    "malignant" "benign"    "benign"    "malignant"
## [139] "malignant" "benign"    "malignant" "benign"    "malignant" "malignant"
## [145] "malignant" "benign"    "malignant" "benign"    "benign"    "malignant"
## [151] "malignant" "benign"    "malignant" "malignant" "malignant" "malignant"
## [157] "benign"    "benign"    "malignant" "malignant" "malignant" "benign"   
## [163] "benign"    "benign"    "malignant" "malignant" "benign"    "benign"   
## [169] "malignant" "malignant" "benign"    "benign"    "malignant" "malignant"
## [175] "malignant" "malignant" "malignant" "benign"    "malignant" "malignant"
## [181] "benign"    "malignant" "benign"    "benign"    "benign"    "benign"   
## [187] "malignant" "benign"    "malignant" "malignant" "malignant" "malignant"
## [193] "malignant" "malignant" "benign"    "malignant" "malignant" "benign"   
## [199] "benign"    "malignant" "malignant" "benign"    "malignant" "benign"   
## [205] "malignant" "malignant" "benign"    "benign"    "benign"    "benign"   
## [211] "malignant" "benign"    "malignant" "malignant" "benign"    "malignant"
## [217] "malignant" "benign"    "malignant" "benign"    "benign"    "malignant"
## [223] "malignant" "malignant" "benign"    "benign"    "malignant" "malignant"
## [229] "benign"    "benign"    "benign"    "malignant" "malignant" "benign"   
## [235] "benign"    "malignant" "benign"    "malignant" "malignant" "benign"   
## [241] "malignant" "malignant" "benign"    "malignant" "malignant" "malignant"
## [247] "malignant" "malignant" "benign"    "benign"    "benign"    "benign"   
## [253] "malignant" "malignant" "benign"    "benign"    "benign"    "benign"   
## [259] "benign"    "benign"    "benign"    "benign"    "benign"    "malignant"
## [265] "benign"    "benign"    "malignant" "benign"    "benign"    "benign"   
## [271] "benign"    "malignant" "benign"    "benign"    "benign"    "benign"   
## [277] "benign"    "malignant" "benign"    "benign"    "benign"    "benign"   
## [283] "benign"    "benign"    "benign"    "malignant" "benign"    "malignant"
## [289] "benign"    "benign"    "benign"    "benign"    "benign"    "malignant"
## [295] "benign"    "benign"    "benign"    "benign"    "benign"    "benign"   
## [301] "benign"    "malignant" "malignant" "benign"    "benign"    "benign"   
## [307] "benign"    "benign"    "benign"    "benign"    "benign"    "benign"   
## [313] "benign"    "malignant" "benign"    "benign"    "malignant" "benign"   
## [319] "benign"    "benign"    "benign"    "malignant" "benign"    "benign"   
## [325] "benign"    "benign"    "benign"    "benign"    "benign"    "benign"   
## [331] "malignant" "benign"    "malignant" "malignant" "benign"    "benign"   
## [337] "malignant" "malignant" "malignant" "benign"    "malignant" "benign"   
## [343] "malignant" "benign"    "benign"    "benign"    "benign"    "benign"   
## [349] "benign"    "benign"    "benign"    "malignant" "benign"    "benign"   
## [355] "benign"    "benign"    "benign"    "malignant" "benign"    "malignant"
## [361] "benign"    "benign"    "benign"    "benign"    "benign"    "benign"   
## [367] "benign"    "benign"    "benign"    "benign"    "benign"    "benign"   
## [373] "benign"    "benign"    "benign"    "benign"    "benign"    "benign"   
## [379] "benign"    "benign"    "benign"    "benign"    "benign"    "benign"   
## [385] "benign"    "benign"    "malignant" "malignant" "malignant" "malignant"
## [391] "malignant" "benign"    "malignant" "benign"    "benign"    "benign"   
## [397] "malignant" "malignant" "benign"    "benign"    "benign"    "malignant"
## [403] "benign"    "malignant" "malignant" "benign"    "malignant" "benign"   
## [409] "benign"    "benign"    "benign"    "benign"    "benign"    "malignant"
## [415] "malignant" "benign"    "benign"    "benign"    "malignant" "malignant"
## [421] "benign"    "benign"    "benign"    "benign"    "benign"    "benign"   
## [427] "benign"    "benign"    "benign"    "benign"    "benign"    "benign"   
## [433] "benign"    "malignant" "benign"    "malignant" "benign"    "benign"   
## [439] "benign"    "benign"    "benign"    "benign"    "benign"    "malignant"
## [445] "benign"    "benign"    "benign"    "benign"    "benign"    "benign"   
## [451] "benign"    "malignant" "benign"    "benign"    "benign"    "benign"   
## [457] "benign"    "benign"    "malignant" "benign"    "benign"    "benign"   
## [463] "benign"    "benign"    "benign"    "benign"    "benign"    "malignant"
## [469] "malignant" "benign"    "benign"    "benign"    "benign"    "benign"   
## [475] "malignant" "benign"    "benign"    "malignant" "malignant"
## 
## $importance
##     Bare.nuclei     Bl.cromatin      Cell.shape       Cell.size    Cl.thickness 
##       28.171016        2.506192        2.740277       37.360946        9.835175 
##    Epith.c.size   Marg.adhesion         Mitoses Normal.nucleoli 
##        2.965481        7.317661        3.170586        5.932666 
## 
## $terms
## Class ~ Cl.thickness + Cell.size + Cell.shape + Marg.adhesion + 
##     Epith.c.size + Bare.nuclei + Bl.cromatin + Normal.nucleoli + 
##     Mitoses
## attr(,"variables")
## list(Class, Cl.thickness, Cell.size, Cell.shape, Marg.adhesion, 
##     Epith.c.size, Bare.nuclei, Bl.cromatin, Normal.nucleoli, 
##     Mitoses)
## attr(,"factors")
##                 Cl.thickness Cell.size Cell.shape Marg.adhesion Epith.c.size
## Class                      0         0          0             0            0
## Cl.thickness               1         0          0             0            0
## Cell.size                  0         1          0             0            0
## Cell.shape                 0         0          1             0            0
## Marg.adhesion              0         0          0             1            0
## Epith.c.size               0         0          0             0            1
## Bare.nuclei                0         0          0             0            0
## Bl.cromatin                0         0          0             0            0
## Normal.nucleoli            0         0          0             0            0
## Mitoses                    0         0          0             0            0
##                 Bare.nuclei Bl.cromatin Normal.nucleoli Mitoses
## Class                     0           0               0       0
## Cl.thickness              0           0               0       0
## Cell.size                 0           0               0       0
## Cell.shape                0           0               0       0
## Marg.adhesion             0           0               0       0
## Epith.c.size              0           0               0       0
## Bare.nuclei               1           0               0       0
## Bl.cromatin               0           1               0       0
## Normal.nucleoli           0           0               1       0
## Mitoses                   0           0               0       1
## attr(,"term.labels")
## [1] "Cl.thickness"    "Cell.size"       "Cell.shape"      "Marg.adhesion"  
## [5] "Epith.c.size"    "Bare.nuclei"     "Bl.cromatin"     "Normal.nucleoli"
## [9] "Mitoses"        
## attr(,"order")
## [1] 1 1 1 1 1 1 1 1 1
## attr(,"intercept")
## [1] 1
## attr(,"response")
## [1] 1
## attr(,".Environment")
## <environment: 0x000002415208ae78>
## 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 AdaBoost dengan 50 learner menggunakan 50 pohon keputusan sederhana (decision stump), sedangkan model dengan 100 learner menggunakan 100 pohon dan model dengan 200 learner menggunakan 200 pohon keputusan untuk mengklasifikasikan data ke dalam kelas benign dan malignant. Ketiga model menggabungkan hasil prediksi dari setiap pohon dengan bobot masing-masing untuk menentukan kelas akhir. Penambahan jumlah learner memungkinkan model mempelajari pola data melalui lebih banyak pohon keputusan, tetapi tidak selalu menghasilkan kinerja klasifikasi yang lebih baik. Oleh karena itu, ketiga model perlu dibandingkan menggunakan data testing berdasarkan ukuran evaluasi, seperti akurasi, sensitivitas, spesifisitas, dan confusion matrix, untuk menentukan model yang memberikan hasil klasifikasi terbaik.

4.5 Prediksi dan Evaluasi Model

prediksi.ada <- lapply(model.ada, function(model) {
  hasil <- predict(model, newdata = bc.test)

  factor(
    hasil$class,
    levels = levels(bc.test$Class)
  )
})

# Membandingkan aktual dan prediksi untuk setiap model
hasil.prediksi <- lapply(names(prediksi.ada), function(nama) {
  data.frame(
    Model = nama,
    Aktual = bc.test$Class,
    Prediksi = prediksi.ada[[nama]]
  )
})

names(hasil.prediksi) <- names(prediksi.ada)

# Menampilkan 10 prediksi pertama untuk setiap model
lapply(hasil.prediksi, head, 10)
## $AdaBoost_50
##          Model    Aktual  Prediksi
## 1  AdaBoost_50    benign malignant
## 2  AdaBoost_50    benign    benign
## 3  AdaBoost_50    benign    benign
## 4  AdaBoost_50    benign    benign
## 5  AdaBoost_50    benign    benign
## 6  AdaBoost_50 malignant malignant
## 7  AdaBoost_50    benign    benign
## 8  AdaBoost_50    benign    benign
## 9  AdaBoost_50    benign    benign
## 10 AdaBoost_50 malignant malignant
## 
## $AdaBoost_100
##           Model    Aktual  Prediksi
## 1  AdaBoost_100    benign malignant
## 2  AdaBoost_100    benign    benign
## 3  AdaBoost_100    benign    benign
## 4  AdaBoost_100    benign    benign
## 5  AdaBoost_100    benign    benign
## 6  AdaBoost_100 malignant malignant
## 7  AdaBoost_100    benign    benign
## 8  AdaBoost_100    benign    benign
## 9  AdaBoost_100    benign    benign
## 10 AdaBoost_100 malignant malignant
## 
## $AdaBoost_200
##           Model    Aktual  Prediksi
## 1  AdaBoost_200    benign malignant
## 2  AdaBoost_200    benign    benign
## 3  AdaBoost_200    benign    benign
## 4  AdaBoost_200    benign    benign
## 5  AdaBoost_200    benign    benign
## 6  AdaBoost_200 malignant malignant
## 7  AdaBoost_200    benign    benign
## 8  AdaBoost_200    benign    benign
## 9  AdaBoost_200    benign    benign
## 10 AdaBoost_200 malignant malignant
# B3. Confusion Matrix

confusion.ada <- lapply(names(prediksi.ada), function(nama) {
  confusionMatrix(
    data = prediksi.ada[[nama]],
    reference = bc.test$Class,
    positive = "malignant"
  )
})

names(confusion.ada) <- names(prediksi.ada)

# Menampilkan confusion matrix setiap model
confusion.ada$AdaBoost_50
## Confusion Matrix and Statistics
## 
##            Reference
## Prediction  benign malignant
##   benign       130         4
##   malignant      3        67
##                                           
##                Accuracy : 0.9657          
##                  95% CI : (0.9306, 0.9861)
##     No Information Rate : 0.652           
##     P-Value [Acc > NIR] : <2e-16          
##                                           
##                   Kappa : 0.9241          
##                                           
##  Mcnemar's Test P-Value : 1               
##                                           
##             Sensitivity : 0.9437          
##             Specificity : 0.9774          
##          Pos Pred Value : 0.9571          
##          Neg Pred Value : 0.9701          
##              Prevalence : 0.3480          
##          Detection Rate : 0.3284          
##    Detection Prevalence : 0.3431          
##       Balanced Accuracy : 0.9606          
##                                           
##        'Positive' Class : malignant       
## 
confusion.ada$AdaBoost_100
## 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.ada$AdaBoost_200
## 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       
## 
# B4. Menghitung Ukuran Evaluasi Klasifikasi

hitung.evaluasi <- function(aktual, prediksi) {

  # Baris = aktual, kolom = prediksi
  tab <- table(
    Aktual = factor(aktual, levels = c("benign", "malignant")),
    Prediksi = factor(prediksi, levels = c("benign", "malignant"))
  )

  TN <- unname(tab["benign", "benign"])
  FP <- unname(tab["benign", "malignant"])
  FN <- unname(tab["malignant", "benign"])
  TP <- unname(tab["malignant", "malignant"])

  Accuracy <- (TP + TN) / sum(tab)

  Precision <- if ((TP + FP) > 0) {
    TP / (TP + FP)
  } else {
    NA_real_
  }

  Recall <- if ((TP + FN) > 0) {
    TP / (TP + FN)
  } else {
    NA_real_
  }

  Specificity <- if ((TN + FP) > 0) {
    TN / (TN + FP)
  } else {
    NA_real_
  }

  F1_Score <- if (
    is.finite(Precision) &&
    is.finite(Recall) &&
    (Precision + Recall) > 0
  ) {
    2 * Precision * Recall / (Precision + Recall)
  } else {
    NA_real_
  }

  data.frame(
    TN = TN,
    FP = FP,
    FN = FN,
    TP = TP,
    Accuracy = Accuracy,
    Precision = Precision,
    Recall = Recall,
    Specificity = Specificity,
    F1_Score = F1_Score
  )
}

evaluasi.ada <- do.call(
  rbind,
  lapply(names(prediksi.ada), function(nama) {
    hasil <- hitung.evaluasi(
      aktual = bc.test$Class,
      prediksi = prediksi.ada[[nama]]
    )

    hasil$Model <- nama
    hasil
  })
)

# Mengatur urutan kolom
evaluasi.ada <- evaluasi.ada[
  ,
  c(
    "Model", "TN", "FP", "FN", "TP",
    "Accuracy", "Precision", "Recall",
    "Specificity", "F1_Score"
  )
]

# Menampilkan tabel evaluasi
evaluasi.tampil <- evaluasi.ada
kolom.metrik <- c(
  "Accuracy", "Precision", "Recall",
  "Specificity", "F1_Score"
)

evaluasi.tampil[kolom.metrik] <-
  lapply(evaluasi.tampil[kolom.metrik], round, 4)

knitr::kable(
  evaluasi.tampil,
  caption = "Perbandingan Evaluasi Model AdaBoost"
)
Perbandingan Evaluasi Model AdaBoost
Model TN FP FN TP Accuracy Precision Recall Specificity F1_Score
AdaBoost_50 130 3 4 67 0.9657 0.9571 0.9437 0.9774 0.9504
AdaBoost_100 130 3 4 67 0.9657 0.9571 0.9437 0.9774 0.9504
AdaBoost_200 130 3 5 66 0.9608 0.9565 0.9296 0.9774 0.9429

Berdasarkan tabel perbandingan evaluasi model AdaBoost, model AdaBoost dengan 50 dan 100 learner menghasilkan kinerja yang sama, dengan nilai akurasi sebesar 0,9657, presisi 0,9571, recall 0,9437, spesifisitas 0,9774, dan F1-Score 0,9504. Kedua model menghasilkan 130 True Negative (TN), 3 False Positive (FP), 4 False Negative (FN), dan 67 True Positive (TP). Sementara itu, model AdaBoost dengan 200 learner menghasilkan akurasi sebesar 0,9608, presisi 0,9565, recall 0,9296, spesifisitas 0,9774, dan F1-Score 0,9429, dengan 130 TN, 3 FP, 5 FN, dan 66 TP. Hasil ini menunjukkan bahwa penambahan jumlah learner dari 50 menjadi 100 tidak mengubah kinerja model, sedangkan penggunaan 200 learner menghasilkan penurunan pada akurasi, recall, dan F1-Score. Dengan demikian, AdaBoost dengan 50 atau 100 learner menjadi pilihan terbaik berdasarkan hasil evaluasi tersebut, karena keduanya memiliki nilai evaluasi yang lebih tinggi dibandingkan model dengan 200 learner, kecuali spesifisitas yang sama pada ketiga model.

BAB V KESIMPULAN

5.1 Kesimpulan

Boosting merupakan metode ensemble learning yang membangun model secara berurutan untuk memperbaiki kesalahan prediksi dari model sebelumnya. Metode ini menggabungkan beberapa weak learner yang memiliki kemampuan prediksi terbatas menjadi satu model akhir yang lebih kuat atau strong learner. Berbeda dengan Bagging yang membangun model secara independen, Boosting menerapkan prinsip sequential learning, yaitu setiap model baru dibentuk berdasarkan informasi dari hasil model sebelumnya sehingga mampu meningkatkan ketepatan prediksi.

Adaptive Boosting atau AdaBoost merupakan salah satu algoritma Boosting yang bekerja dengan menyesuaikan bobot setiap observasi pada data latih. Observasi yang salah diklasifikasikan akan memperoleh bobot lebih besar sehingga model berikutnya lebih berfokus pada observasi tersebut. Proses ini dilakukan secara berulang, kemudian prediksi dari setiap weak learner digabungkan berdasarkan bobotnya untuk menghasilkan prediksi akhir. Dalam penerapannya, AdaBoost umumnya menggunakan decision stump sebagai model dasar untuk menyelesaikan permasalahan klasifikasi.

Sementara itu, Gradient Boosting membangun model secara bertahap dengan meminimalkan fungsi kerugian (loss function). Setiap model baru berusaha memperbaiki kesalahan model sebelumnya berdasarkan gradien fungsi kerugian. Algoritma ini dapat digunakan untuk permasalahan klasifikasi maupun regresi, dengan kinerja yang dipengaruhi oleh beberapa parameter, seperti jumlah pohon, kedalaman pohon, dan learning rate. Dengan demikian, AdaBoost dan Gradient Boosting memiliki prinsip dasar yang sama, yaitu membangun model secara berurutan untuk meningkatkan kemampuan prediksi, tetapi menggunakan mekanisme yang berbeda dalam memperbaiki kesalahan model.

Berdasarkan hasil analisis, metode AdaBoost dengan jumlah learner 50, 100, dan 200 menunjukkan kemampuan yang baik dalam mengklasifikasikan data ke dalam kelas benign dan malignant. Model AdaBoost dengan 50 dan 100 learner menghasilkan kinerja yang sama, yaitu akurasi sebesar 96,57%, presisi 95,71%, recall 94,37%, spesifisitas 97,74%, dan F1-Score 95,04%. Sementara itu, model dengan 200 learner menghasilkan akurasi 96,08% dan F1-Score 94,29%, dengan nilai recall yang lebih rendah. Dengan demikian, model AdaBoost dengan 50 atau 100 learner merupakan pilihan terbaik berdasarkan hasil evaluasi karena memberikan kinerja klasifikasi yang lebih baik dibandingkan model dengan 200 learner. Selain itu, penambahan jumlah learner dari 50 menjadi 100 tidak meningkatkan kinerja model, sehingga penggunaan 50 learner sudah cukup untuk memperoleh hasil klasifikasi yang terbaik pada pengujian ini.

DAFTAR PUSTAKA

Abdurrahman, G. (2022). Klasifikasi penyakit diabetes melitus menggunakan Adaboost Classifier. JUSTINDO (Jurnal Sistem dan Teknologi Informasi Indonesia), 7(1), 59–66.

Afrizal, M. R. (2022). XGBoost dengan Random Search hyper-parameter tuning untuk klasifikasi situs phishing. Jurnal Ilmu Komputer, 15(1), 40–47.

Fallahi, P. A. N., Nurdiati, S., & Bukhari, F. (2023). Implementasi metode regresi Random Forest dan Gradient Boosting pada data jumlah hotspot di Kalimantan. Institut Pertanian Bogor.

Fransiska, H. (2026). Modul Praktikum Machine Learning and Modern Prediction. Laboratorium Matematika Universitas Bengkulu.

Majid, A. M., & Miharja, M. N. D. (2022). Penerapan metode discretization dan AdaBoost untuk meningkatkan akurasi algoritma klasifikasi dalam memprediksi penyakit jantung. Indonesian Journal of Business Intelligence (IJUBI), 5(2), 70–75.

Sihombing, P. R. (2025). Comparative analysis of ensemble bagging and boosting methods: A systematic literature review. Jurnal Bayesian. Jurnal Ilmiah Statistika dan Ekonometrika, 5(2), 188–204.