Perkembangan teknologi informasi telah mendorong pemanfaatan data dalam berbagai bidang sehingga diperlukan metode analisis yang mampu menghasilkan prediksi secara tepat. Salah satu pendekatan yang digunakan untuk mengolah data dan membangun model prediksi adalah machine learning. Dalam penerapannya, pemilihan metode yang sesuai menjadi penting karena setiap algoritma memiliki karakteristik dan kemampuan yang berbeda dalam menyelesaikan permasalahan klasifikasi maupun regresi. Salah satu pendekatan dalam machine learning yang dapat digunakan untuk meningkatkan kinerja prediksi adalah ensemble learning, yaitu teknik yang menggabungkan beberapa model untuk menghasilkan prediksi akhir (Cendani dan Wibowo, 2022).
Ensemble learning memiliki beberapa metode, di antaranya bagging, boosting dan stacking. Ketiga metode tersebut memiliki prinsip penggabungan model yang berbeda. Boosting merupakan metode ensemble learning yang membangun model secara bertahap dengan memperhatikan kesalahan prediksi dari model sebelumnya. Melalui proses tersebut, model berikutnya diarahkan untuk memperbaiki kesalahan yang masih terjadi sehingga gabungan model diharapkan menghasilkan prediksi yang lebih baik dibandingkan penggunaan model tunggal (Cendani dan Wibowo, 2022).
Selain digunakan untuk klasifikasi, boosting juga dapat diterapkan pada permasalahan regresi. Klasifikasi bertujuan untuk memprediksi kategori atau kelas suatu observasi, sedangkan regresi digunakan untuk memprediksi nilai numerik. Penerapan kedua jenis analisis tersebut memberikan kesempatan untuk memahami bagaimana prinsip boosting digunakan pada target yang berbeda. Oleh karena itu, pemahaman mengenai konsep dasar dan mekanisme kerja boosting diperlukan sebelum metode tersebut diimplementasikan menggunakan perangkat lunak statistik, seperti R (Lutfiani dkk., 2023)
Berdasarkan latar belakang tersebut, rumusan masalah dalam praktikum ini adalah sebagai berikut:
Bagaimana cara memahami Boosting sebagai metode ensemble learning?
Bagaimana cara mengimplementasikan Boosting untuk kasus klasifikasi dan regresi menggunakan R?
Bagaimana cara mengevaluasi performa model menggunakan ukuran evaluasi yang sesuai?
Tujuan yang ingin dicapai dalam praktikum ini adalah sebagai berikut:
Mahasiswa dapat memahami Boosting sebagai metode ensemble learning.
Mahasiswa dapat mengimplementasikan Boosting* untuk kasus klasifikasi dan regresi menggunakan R.
Mahasiswa dapat mengevaluasi performa model menggunakan ukuran evaluasi yang sesuai.
Ensemble learning merupakan pendekatan yang menggabungkan beberapa model dasar (base learner) agar diperoleh model gabungan yang memiliki kemampuan prediksi lebih baik. Boosting memiliki tujuan meningkatkan performa prediksi melalui gabungan beberapa model, tetapi cara model-model tersebut dibentuk berbeda secara mendasar. Perbedaan cara pembentukan model inilah yang menjadi karakter utama Boosting dan membedakannya dari Bagging (Fransiska, 2026).
Boosting adalah metode ensemble yang membentuk serangkaian model sederhana secara berurutan (sequential). Model berikutnya dibangun dengan memperhatikan kesalahan yang masih dilakukan oleh model sebelumnya. Oleh karena itu, kekuatan Boosting bukan berasal dari satu model yang sangat kompleks, melainkan dari gabungan banyak model sederhana yang secara bertahap memperbaiki hasil prediksi. 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. 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).
Beberapa algoritma boosting yang umum digunakan adalah sebagai berikut (Maulana, Astuti dan Basysyar, 2023).
Adaptive Boosting (AdaBoost) merupakan algoritma boosting yang mengubah bobot observasi selama proses pembelajaran. Observasi yang salah diklasifikasikan diberi bobot lebih besar sehingga model berikutnya lebih memperhatikan observasi tersebut. Prediksi akhir klasifikasi ditentukan melalui penggabungan prediksi model dasar dengan bobot tertentu.
Gradient Boosting membangun model secara bertahap dengan memanfaatkan gradien dari fungsi kerugian untuk menentukan arah perbaikan model. Pada permasalahan regresi, proses ini dapat dilakukan dengan mempelajari residual atau pendekatan terhadap residual sehingga kesalahan prediksi dapat dikurangi secara bertahap.
Extreme Gradient Boosting (XGBoost) merupakan pengembangan gradient boosting yang dirancang untuk mendukung proses pembelajaran yang efisien dan pengendalian kompleksitas model. Algoritma ini dapat digunakan untuk klasifikasi maupun regresi.
Light Gradient Boosting Machine (LightGBM) merupakan algoritma berbasis gradient boosting yang dirancang untuk efisiensi komputasi, khususnya pada dataset berukuran besar. Algoritma ini dapat diterapkan pada tugas klasifikasi dan regresi.
Categorical Boosting (CatBoost) merupakan algoritma boosting yang dirancang untuk menangani variabel kategorik dengan pendekatan tertentu. Algoritma ini dapat digunakan untuk klasifikasi maupun regresi.
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.
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.
Langkah-langkah analisis data pada penelitian ini adalah sebagai berikut:
Menyiapkan data yang akan di analisis.
Memanggil dataset BreastCancer dan melakukan pemeriksaan struktur data.
Melakukan prapemrosesan data dengan menangani nilai yang hilang, menghapus variabel identitas, serta mengubah variabel kelas menjadi faktor.
Membagi data menjadi 80% data latih dan 20% data uji menggunakan metode stratified sampling.
Membentuk model AdaBoost menggunakan algoritma decision tree dengan kedalaman maksimum satu sebagai weak learner.
Melatih tiga model AdaBoost dengan jumlah learner sebanyak 50, 100 dan 200.
Melakukan prediksi terhadap data uji menggunakan masing-masing model.
Membentuk confusion matrix untuk mengevaluasi hasil klasifikasi setiap model.
Menghitung metrik evaluasi berupa Accuracy, Precision, Recall, Specificity dan F1-score.
Membandingkan hasil evaluasi ketiga model untuk menentukan model AdaBoost dengan performa terbaik.
Package yang digunakan pada praktikum ini adalah mlbench, adabag, rpart dan knitr. Package mlbench menyediakan dataset BreastCancer, adabag digunakan untuk membangun model AdaBoost, rpart digunakan untuk mengatur decision stump sebagai weak learner, sedangkan knitr digunakan untuk menampilkan hasil evaluasi dalam bentuk tabel.
## 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# Memuat dataset BreastCancer
data("BreastCancer", package = "mlbench")
data_bc <- BreastCancer
# Memeriksa data
dim(data_bc)## [1] 699 11## '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 ...## Id Cl.thickness Cell.size Cell.shape Marg.adhesion Epith.c.size
## 1 1000025 5 1 1 1 2
## 2 1002945 5 4 4 5 7
## 3 1015425 3 1 1 1 2
## 4 1016277 6 8 8 1 3
## 5 1017023 4 1 1 3 2
## 6 1017122 8 10 10 8 7
## Bare.nuclei Bl.cromatin Normal.nucleoli Mitoses Class
## 1 1 3 1 1 benign
## 2 10 3 2 1 benign
## 3 2 3 1 1 benign
## 4 4 3 7 1 benign
## 5 1 3 1 1 benign
## 6 10 9 7 1 malignant## Id Cl.thickness Cell.size Cell.shape Marg.adhesion
## 0 0 0 0 0
## Epith.c.size Bare.nuclei Bl.cromatin Normal.nucleoli Mitoses
## 0 16 0 0 0
## Class
## 0# Menghapus missing value
data_bc <- na.omit(data_bc)
# Menghapus variabel ID karena bukan prediktor
data_bc$Id <- NULL
# Memastikan variabel target berbentuk faktor
data_bc$Class <- factor(
data_bc$Class,
levels = c("benign", "malignant")
)
# Memeriksa kembali data
dim(data_bc)## [1] 683 10## Cl.thickness Cell.size Cell.shape Marg.adhesion Epith.c.size
## 0 0 0 0 0
## Bare.nuclei Bl.cromatin Normal.nucleoli Mitoses Class
## 0 0 0 0 0##
## benign malignant
## 444 239Dataset yang digunakan adalah dataset BreastCancer yang memiliki 699 observasi dan 11 variabel. Terdapat 16 missing value pada variabel Bare.nuclei. Setelah menggunakan na.omit(), jumlah observasi berkurang menjadi 683 karena 16 baris yang mengandung missing value dihapus. Hasil pemeriksaan kembali menunjukkan bahwa seluruh variabel sudah tidak memiliki missing value, sehingga data siap digunakan untuk analisis selanjutnya.
Data dibagi menjadi 80% data latih dan 20% data uji. Pembagian dilakukan secara stratified sampling agar proporsi kelas benign dan malignant relatif terjaga pada kedua kelompok data.
set.seed(22)
# Membagi data berdasarkan kelas
indeks_train <- unlist(
lapply(
split(seq_len(nrow(data_bc)), data_bc$Class),
function(i) {
sample(i, size = floor(0.8 * length(i)))
}
),
use.names = FALSE
)
# Data latih dan data uji
data_train <- data_bc[indeks_train, ]
data_test <- data_bc[-indeks_train, ]
# Memeriksa jumlah data
dim(data_train)## [1] 546 10## [1] 137 10##
## benign malignant
## 355 191##
## benign malignant
## 89 48Pembagian data dilakukan menggunakan metode stratified sampling dengan proporsi 80% data latih dan 20% data uji berdasarkan kelas. Hasilnya, diperoleh 546 data latih dan 137 data uji. Data latih terdiri dari 355 kelas benign dan 191 kelas malignant, sedangkan data uji terdiri dari 89 kelas benign dan 48 kelas malignant.
Algoritma AdaBoost dibangun menggunakan fungsi boosting() dari paket adabag. Jumlah learner yang dibandingkan adalah 50, 100 dan 200.
Model pertama menggunakan 50 learner. Setiap pohon keputusan dibatasi pada kedalaman maksimum satu. Model AdaBoost dengan 50 learner digunakan sebagai model awal untuk mengetahui kemampuan klasifikasi menggunakan gabungan 50 pohon keputusan
set.seed(22)
class_mod50 <- adabag::boosting(
Class ~ .,
data = data_train,
mfinal = 50,
control = rpart::rpart.control(
maxdepth = 1,
minsplit = 2,
cp = 0
)
)
print(class_mod50)## $formula
## Class ~ .
##
## $trees
## $trees[[1]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 182 benign (0.66666667 0.33333333)
## 2) Cell.size=1,2,3 383 25 benign (0.93472585 0.06527415) *
## 3) Cell.size=4,5,6,7,8,9,10 163 6 malignant (0.03680982 0.96319018) *
##
## $trees[[2]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 220 benign (0.59706960 0.40293040)
## 2) Bare.nuclei=1,2,3 336 29 benign (0.91369048 0.08630952) *
## 3) Bare.nuclei=4,5,6,7,8,9,10 210 19 malignant (0.09047619 0.90952381) *
##
## $trees[[3]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 238 benign (0.56410256 0.43589744)
## 2) Cell.size=1 225 6 benign (0.97333333 0.02666667) *
## 3) Cell.size=2,3,4,5,6,7,8,9,10 321 89 malignant (0.27725857 0.72274143) *
##
## $trees[[4]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 195 benign (0.6428571 0.3571429)
## 2) Cl.thickness=1,2,3,4,5,6 407 72 benign (0.8230958 0.1769042) *
## 3) Cl.thickness=7,8,9,10 139 16 malignant (0.1151079 0.8848921) *
##
## $trees[[5]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 232 benign (0.5750916 0.4249084)
## 2) Bare.nuclei=1,4,7,8 317 51 benign (0.8391167 0.1608833) *
## 3) Bare.nuclei=2,3,5,6,9,10 229 48 malignant (0.2096070 0.7903930) *
##
## $trees[[6]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 250 benign (0.5421245 0.4578755)
## 2) Normal.nucleoli=1,2,7,8 346 87 benign (0.7485549 0.2514451) *
## 3) Normal.nucleoli=3,4,5,6,9,10 200 37 malignant (0.1850000 0.8150000) *
##
## $trees[[7]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 271 malignant (0.4963370 0.5036630)
## 2) Bl.cromatin=1,2,3,4 361 110 benign (0.6952909 0.3047091) *
## 3) Bl.cromatin=5,6,7,8,9,10 185 20 malignant (0.1081081 0.8918919) *
##
## $trees[[8]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 249 malignant (0.4560440 0.5439560)
## 2) Cell.shape=1,2 179 41 benign (0.7709497 0.2290503) *
## 3) Cell.shape=3,4,5,6,7,8,9,10 367 111 malignant (0.3024523 0.6975477) *
##
## $trees[[9]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 270 benign (0.5054945 0.4945055)
## 2) Cl.thickness=1,2,3,4,5,6,7,8 460 184 benign (0.6000000 0.4000000) *
## 3) Cl.thickness=9,10 86 0 malignant (0.0000000 1.0000000) *
##
## $trees[[10]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 232 malignant (0.424908425 0.575091575)
## 2) Marg.adhesion=1,2,3,4,5 437 206 benign (0.528604119 0.471395881) *
## 3) Marg.adhesion=6,7,8,9,10 109 1 malignant (0.009174312 0.990825688) *
##
## $trees[[11]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 232 malignant (0.4249084 0.5750916)
## 2) Bare.nuclei=1,7 159 40 benign (0.7484277 0.2515723) *
## 3) Bare.nuclei=2,3,4,5,6,8,9,10 387 113 malignant (0.2919897 0.7080103) *
##
## $trees[[12]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 235 malignant (0.43040293 0.56959707)
## 2) Marg.adhesion=1,2,3,4,5 440 211 benign (0.52045455 0.47954545) *
## 3) Marg.adhesion=6,7,8,9,10 106 6 malignant (0.05660377 0.94339623) *
##
## $trees[[13]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 243 malignant (0.4450549 0.5549451)
## 2) Normal.nucleoli=2,3,7,8 148 39 benign (0.7364865 0.2635135) *
## 3) Normal.nucleoli=1,4,5,6,9,10 398 134 malignant (0.3366834 0.6633166) *
##
## $trees[[14]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 249 malignant (0.4560440 0.5439560)
## 2) Marg.adhesion=1,2,3,4,5 457 208 benign (0.5448578 0.4551422) *
## 3) Marg.adhesion=6,7,8,9,10 89 0 malignant (0.0000000 1.0000000) *
##
## $trees[[15]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 251 malignant (0.4597070 0.5402930)
## 2) Cl.thickness=1,2,3,4,5,6,7,8 470 219 benign (0.5340426 0.4659574) *
## 3) Cl.thickness=9,10 76 0 malignant (0.0000000 1.0000000) *
##
## $trees[[16]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 218 malignant (0.3992674 0.6007326)
## 2) Bl.cromatin=1,2,3,6 297 126 benign (0.5757576 0.4242424) *
## 3) Bl.cromatin=4,5,7,8,9,10 249 47 malignant (0.1887550 0.8112450) *
##
## $trees[[17]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 218 malignant (0.3992674 0.6007326)
## 2) Cl.thickness=1,2 70 7 benign (0.9000000 0.1000000) *
## 3) Cl.thickness=3,4,5,6,7,8,9,10 476 155 malignant (0.3256303 0.6743697) *
##
## $trees[[18]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 249 malignant (0.4560440 0.5439560)
## 2) Cl.thickness=1,2,3,4,5,6,7,8 473 224 benign (0.5264271 0.4735729) *
## 3) Cl.thickness=9,10 73 0 malignant (0.0000000 1.0000000) *
##
## $trees[[19]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 247 malignant (0.4523810 0.5476190)
## 2) Cl.thickness=1,2,3,4,5,6,7,8 481 234 benign (0.5135135 0.4864865) *
## 3) Cl.thickness=9,10 65 0 malignant (0.0000000 1.0000000) *
##
## $trees[[20]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 233 malignant (0.42673993 0.57326007)
## 2) Normal.nucleoli=1,2,4,5,6,7,8 440 216 benign (0.50909091 0.49090909) *
## 3) Normal.nucleoli=3,9,10 106 9 malignant (0.08490566 0.91509434) *
##
## $trees[[21]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 210 malignant (0.38461538 0.61538462)
## 2) Bl.cromatin=1,6 52 5 benign (0.90384615 0.09615385) *
## 3) Bl.cromatin=2,3,4,5,7,8,9,10 494 163 malignant (0.32995951 0.67004049) *
##
## $trees[[22]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 241 malignant (0.4413919 0.5586081)
## 2) Cell.shape=1 81 14 benign (0.8271605 0.1728395) *
## 3) Cell.shape=2,3,4,5,6,7,8,9,10 465 174 malignant (0.3741935 0.6258065) *
##
## $trees[[23]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 260 benign (0.52380952 0.47619048)
## 2) Marg.adhesion=1,2,3,4,5 485 203 benign (0.58144330 0.41855670) *
## 3) Marg.adhesion=6,7,8,9,10 61 4 malignant (0.06557377 0.93442623) *
##
## $trees[[24]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 240 malignant (0.4395604 0.5604396)
## 2) Bare.nuclei=1,3,7,8 265 103 benign (0.6113208 0.3886792) *
## 3) Bare.nuclei=2,4,5,6,9,10 281 78 malignant (0.2775801 0.7224199) *
##
## $trees[[25]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 237 malignant (0.4340659 0.5659341)
## 2) Cl.thickness=1,2,3,4,5,6,7,8 471 234 benign (0.5031847 0.4968153) *
## 3) Cl.thickness=9,10 75 0 malignant (0.0000000 1.0000000) *
##
## $trees[[26]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 231 malignant (0.4230769 0.5769231)
## 2) Normal.nucleoli=2,4,7 102 20 benign (0.8039216 0.1960784) *
## 3) Normal.nucleoli=1,3,5,6,8,9,10 444 149 malignant (0.3355856 0.6644144) *
##
## $trees[[27]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 245 malignant (0.4487179 0.5512821)
## 2) Cell.size=1,2,3,4 404 184 benign (0.5445545 0.4554455) *
## 3) Cell.size=5,6,7,8,9,10 142 25 malignant (0.1760563 0.8239437) *
##
## $trees[[28]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 260 malignant (0.4761905 0.5238095)
## 2) Normal.nucleoli=1,2,3,4,5,6,7,8 494 234 benign (0.5263158 0.4736842) *
## 3) Normal.nucleoli=9,10 52 0 malignant (0.0000000 1.0000000) *
##
## $trees[[29]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 220 malignant (0.4029304 0.5970696)
## 2) Bare.nuclei=1,2,3 168 64 benign (0.6190476 0.3809524) *
## 3) Bare.nuclei=4,5,6,7,8,9,10 378 116 malignant (0.3068783 0.6931217) *
##
## $trees[[30]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 254 malignant (0.4652015 0.5347985)
## 2) Normal.nucleoli=1,2,3,4,5,6,7,8 488 234 benign (0.5204918 0.4795082) *
## 3) Normal.nucleoli=9,10 58 0 malignant (0.0000000 1.0000000) *
##
## $trees[[31]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 240 malignant (0.43956044 0.56043956)
## 2) Normal.nucleoli=2 46 3 benign (0.93478261 0.06521739) *
## 3) Normal.nucleoli=1,3,4,5,6,7,8,9,10 500 197 malignant (0.39400000 0.60600000) *
##
## $trees[[32]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 248 malignant (0.4542125 0.5457875)
## 2) Cl.thickness=1,2,3,4,5,6,7,8 490 242 benign (0.5061224 0.4938776) *
## 3) Cl.thickness=9,10 56 0 malignant (0.0000000 1.0000000) *
##
## $trees[[33]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 264 malignant (0.4835165 0.5164835)
## 2) Normal.nucleoli=1,2,3,5,7,8 405 170 benign (0.5802469 0.4197531) *
## 3) Normal.nucleoli=4,6,9,10 141 29 malignant (0.2056738 0.7943262) *
##
## $trees[[34]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 256 malignant (0.46886447 0.53113553)
## 2) Marg.adhesion=1,2,3,4,5 486 234 benign (0.51851852 0.48148148) *
## 3) Marg.adhesion=6,7,8,9,10 60 4 malignant (0.06666667 0.93333333) *
##
## $trees[[35]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 229 malignant (0.4194139 0.5805861)
## 2) Normal.nucleoli=2,4,7 110 35 benign (0.6818182 0.3181818) *
## 3) Normal.nucleoli=1,3,5,6,8,9,10 436 154 malignant (0.3532110 0.6467890) *
##
## $trees[[36]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 249 malignant (0.4560440 0.5439560)
## 2) Cl.thickness=1,2,3,4,5,6,7,8 495 246 benign (0.5030303 0.4969697) *
## 3) Cl.thickness=9,10 51 0 malignant (0.0000000 1.0000000) *
##
## $trees[[37]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 237 malignant (0.4340659 0.5659341)
## 2) Mitoses=2 78 14 benign (0.8205128 0.1794872) *
## 3) Mitoses=1,3,4,6,7,8,10 468 173 malignant (0.3696581 0.6303419) *
##
## $trees[[38]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 245 malignant (0.4487179 0.5512821)
## 2) Cell.size=1,2,3,4 401 186 benign (0.5361596 0.4638404) *
## 3) Cell.size=5,6,7,8,9,10 145 30 malignant (0.2068966 0.7931034) *
##
## $trees[[39]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 235 malignant (0.4304029 0.5695971)
## 2) Normal.nucleoli=2,3,4,8 160 55 benign (0.6562500 0.3437500) *
## 3) Normal.nucleoli=1,5,6,7,9,10 386 130 malignant (0.3367876 0.6632124) *
##
## $trees[[40]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 251 benign (0.5402930 0.4597070)
## 2) Normal.nucleoli=1,2,3,4,5,6,7,8 505 210 benign (0.5841584 0.4158416) *
## 3) Normal.nucleoli=9,10 41 0 malignant (0.0000000 1.0000000) *
##
## $trees[[41]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 270 benign (0.5054945 0.4945055)
## 2) Cl.thickness=1,2,3,4,5,6,7,8 498 222 benign (0.5542169 0.4457831) *
## 3) Cl.thickness=9,10 48 0 malignant (0.0000000 1.0000000) *
##
## $trees[[42]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 253 malignant (0.4633700 0.5366300)
## 2) Cl.thickness=1,2,3,4,5,6 425 193 benign (0.5458824 0.4541176) *
## 3) Cl.thickness=7,8,9,10 121 21 malignant (0.1735537 0.8264463) *
##
## $trees[[43]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 248 malignant (0.45421245 0.54578755)
## 2) Marg.adhesion=1,2,3,4,5 480 237 benign (0.50625000 0.49375000) *
## 3) Marg.adhesion=6,7,8,9,10 66 5 malignant (0.07575758 0.92424242) *
##
## $trees[[44]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 226 malignant (0.4139194 0.5860806)
## 2) Bare.nuclei=1,2,3,7 198 81 benign (0.5909091 0.4090909) *
## 3) Bare.nuclei=4,5,6,8,9,10 348 109 malignant (0.3132184 0.6867816) *
##
## $trees[[45]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 226 malignant (0.41391941 0.58608059)
## 2) Bl.cromatin=1 36 3 benign (0.91666667 0.08333333) *
## 3) Bl.cromatin=2,3,4,5,6,7,8,10 510 193 malignant (0.37843137 0.62156863) *
##
## $trees[[46]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 258 malignant (0.4725275 0.5274725)
## 2) Epith.c.size=1,2,3,4,5 425 190 benign (0.5529412 0.4470588) *
## 3) Epith.c.size=6,7,8,9,10 121 23 malignant (0.1900826 0.8099174) *
##
## $trees[[47]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 245 malignant (0.4487179 0.5512821)
## 2) Normal.nucleoli=1,2,3,4,5,7,8 446 217 benign (0.5134529 0.4865471) *
## 3) Normal.nucleoli=6,9,10 100 16 malignant (0.1600000 0.8400000) *
##
## $trees[[48]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 250 malignant (0.4578755 0.5421245)
## 2) Normal.nucleoli=1,2,5,7,8 347 143 benign (0.5878963 0.4121037) *
## 3) Normal.nucleoli=3,4,6,9,10 199 46 malignant (0.2311558 0.7688442) *
##
## $trees[[49]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 244 malignant (0.4468864 0.5531136)
## 2) Cl.thickness=1,2,3,4,5,6,7,8 484 240 benign (0.5041322 0.4958678) *
## 3) Cl.thickness=9,10 62 0 malignant (0.0000000 1.0000000) *
##
## $trees[[50]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 235 malignant (0.4304029 0.5695971)
## 2) Normal.nucleoli=2,5,7 104 31 benign (0.7019231 0.2980769) *
## 3) Normal.nucleoli=1,3,4,6,8,9,10 442 162 malignant (0.3665158 0.6334842) *
##
##
## $weights
## [1] 1.31076505 1.09819339 0.74472981 0.76079564 0.61959018 0.66758383
## [7] 0.54022425 0.43117215 0.27122341 0.25751521 0.44567706 0.20101682
## [13] 0.30188240 0.15434624 0.10087027 0.31374235 0.35514222 0.14112232
## [19] 0.07056116 0.10113228 0.32282202 0.19582807 0.18322326 0.31162511
## [25] 0.12333065 0.29865329 0.23884156 0.08940260 0.25764336 0.09517499
## [31] 0.25703099 0.15447313 0.14484157 0.06697375 0.25938858 0.09814822
## [37] 0.26389356 0.17500584 0.16651895 0.10977757 0.08666640 0.11492642
## [43] 0.02636147 0.24041109 0.22201307 0.16401460 0.09580588 0.10332701
## [49] 0.02665034 0.24770603
##
## $votes
## [,1] [,2]
## [1,] 11.2505492 2.7772163
## [2,] 12.8638946 1.1638709
## [3,] 12.2326916 1.7950738
## [4,] 12.2326916 1.7950738
## [5,] 11.3327143 2.6950511
## [6,] 10.3921564 3.6356090
## [7,] 11.6878565 2.3399089
## [8,] 11.8775494 2.1502160
## [9,] 11.3327143 2.6950511
## [10,] 11.3327143 2.6950511
## [11,] 12.8638946 1.1638709
## [12,] 12.2326916 1.7950738
## [13,] 11.8775494 2.1502160
## [14,] 9.9609843 4.0667812
## [15,] 11.1674244 2.8603410
## [16,] 12.2326916 1.7950738
## [17,] 7.4118848 6.6158806
## [18,] 9.2110922 4.8166733
## [19,] 12.2326916 1.7950738
## [20,] 11.3327143 2.6950511
## [21,] 11.8775494 2.1502160
## [22,] 11.3327143 2.6950511
## [23,] 11.1328196 2.8949458
## [24,] 12.2326916 1.7950738
## [25,] 10.4432776 3.5844879
## [26,] 11.0608563 2.9669091
## [27,] 11.6878565 2.3399089
## [28,] 11.1368862 2.8908792
## [29,] 11.6878565 2.3399089
## [30,] 11.3327143 2.6950511
## [31,] 11.3327143 2.6950511
## [32,] 11.3327143 2.6950511
## [33,] 11.3327143 2.6950511
## [34,] 10.9431267 3.0846387
## [35,] 11.8775494 2.1502160
## [36,] 11.4870022 2.5407632
## [37,] 12.2326916 1.7950738
## [38,] 11.8775494 2.1502160
## [39,] 10.8122822 3.2154833
## [40,] 11.3327143 2.6950511
## [41,] 11.3327143 2.6950511
## [42,] 12.8638946 1.1638709
## [43,] 11.3327143 2.6950511
## [44,] 10.5058194 3.5219461
## [45,] 11.8775494 2.1502160
## [46,] 12.2326916 1.7950738
## [47,] 12.8638946 1.1638709
## [48,] 13.2190368 0.8087287
## [49,] 11.6817213 2.3460441
## [50,] 11.6878565 2.3399089
## [51,] 11.3327143 2.6950511
## [52,] 7.0571130 6.9706524
## [53,] 11.3327143 2.6950511
## [54,] 11.3327143 2.6950511
## [55,] 11.3327143 2.6950511
## [56,] 11.6878565 2.3399089
## [57,] 11.6878565 2.3399089
## [58,] 11.6817213 2.3460441
## [59,] 11.8775494 2.1502160
## [60,] 6.0032553 8.0245101
## [61,] 8.5725196 5.4552459
## [62,] 11.6878565 2.3399089
## [63,] 12.2326916 1.7950738
## [64,] 11.4870022 2.5407632
## [65,] 13.4087297 0.6190358
## [66,] 11.6878565 2.3399089
## [67,] 12.2326916 1.7950738
## [68,] 10.7057141 3.3220514
## [69,] 10.9369915 3.0907739
## [70,] 9.8933320 4.1344334
## [71,] 11.1674244 2.8603410
## [72,] 11.6878565 2.3399089
## [73,] 11.3327143 2.6950511
## [74,] 11.6878565 2.3399089
## [75,] 7.0442447 6.9835207
## [76,] 9.9609843 4.0667812
## [77,] 11.3327143 2.6950511
## [78,] 11.3327143 2.6950511
## [79,] 11.8775494 2.1502160
## [80,] 12.8638946 1.1638709
## [81,] 10.8103379 3.2174275
## [82,] 11.3327143 2.6950511
## [83,] 10.2674471 3.7603184
## [84,] 11.3327143 2.6950511
## [85,] 11.3327143 2.6950511
## [86,] 8.5840919 5.4436735
## [87,] 11.3327143 2.6950511
## [88,] 11.3327143 2.6950511
## [89,] 11.8775494 2.1502160
## [90,] 11.6878565 2.3399089
## [91,] 12.2326916 1.7950738
## [92,] 11.3325740 2.6951914
## [93,] 12.2326916 1.7950738
## [94,] 9.2595515 4.7682140
## [95,] 11.8775494 2.1502160
## [96,] 7.7817321 6.2460334
## [97,] 12.2326916 1.7950738
## [98,] 11.8775494 2.1502160
## [99,] 11.6878565 2.3399089
## [100,] 9.5573425 4.4704229
## [101,] 11.6878565 2.3399089
## [102,] 11.6878565 2.3399089
## [103,] 11.1368862 2.8908792
## [104,] 10.3161265 3.7116389
## [105,] 10.9369915 3.0907739
## [106,] 11.3327143 2.6950511
## [107,] 11.3327143 2.6950511
## [108,] 10.9369915 3.0907739
## [109,] 11.8775494 2.1502160
## [110,] 7.2743093 6.7534561
## [111,] 11.8775494 2.1502160
## [112,] 11.3327143 2.6950511
## [113,] 13.0191421 1.0086234
## [114,] 12.2326916 1.7950738
## [115,] 11.8775494 2.1502160
## [116,] 12.2326916 1.7950738
## [117,] 9.1018554 4.9259101
## [118,] 11.3327143 2.6950511
## [119,] 11.3327143 2.6950511
## [120,] 12.2326916 1.7950738
## [121,] 6.9904521 7.0373133
## [122,] 12.2326916 1.7950738
## [123,] 11.1368862 2.8908792
## [124,] 11.8775494 2.1502160
## [125,] 11.6878565 2.3399089
## [126,] 11.3327143 2.6950511
## [127,] 12.2326916 1.7950738
## [128,] 11.1368862 2.8908792
## [129,] 12.2326916 1.7950738
## [130,] 11.3327143 2.6950511
## [131,] 11.0608563 2.9669091
## [132,] 11.6878565 2.3399089
## [133,] 11.3327143 2.6950511
## [134,] 11.8775494 2.1502160
## [135,] 11.9517501 2.0760153
## [136,] 10.3211560 3.7066094
## [137,] 10.7057141 3.3220514
## [138,] 11.1368862 2.8908792
## [139,] 11.8775494 2.1502160
## [140,] 11.3327143 2.6950511
## [141,] 10.3109642 3.7168012
## [142,] 11.8775494 2.1502160
## [143,] 11.3327143 2.6950511
## [144,] 12.2326916 1.7950738
## [145,] 11.6817213 2.3460441
## [146,] 11.2921337 2.7356317
## [147,] 12.2326916 1.7950738
## [148,] 12.8638946 1.1638709
## [149,] 11.6878565 2.3399089
## [150,] 12.2326916 1.7950738
## [151,] 11.3327143 2.6950511
## [152,] 11.8775494 2.1502160
## [153,] 11.3327143 2.6950511
## [154,] 11.8775494 2.1502160
## [155,] 11.3327143 2.6950511
## [156,] 11.3327143 2.6950511
## [157,] 10.9881127 3.0396528
## [158,] 12.2326916 1.7950738
## [159,] 8.7147164 5.3130491
## [160,] 11.6878565 2.3399089
## [161,] 10.5058194 3.5219461
## [162,] 12.4681718 1.5595937
## [163,] 11.0608563 2.9669091
## [164,] 11.6878565 2.3399089
## [165,] 11.6878565 2.3399089
## [166,] 11.3327143 2.6950511
## [167,] 11.1328196 2.8949458
## [168,] 10.2674471 3.7603184
## [169,] 11.6878565 2.3399089
## [170,] 11.6878565 2.3399089
## [171,] 10.7057141 3.3220514
## [172,] 12.2326916 1.7950738
## [173,] 11.3327143 2.6950511
## [174,] 12.2326916 1.7950738
## [175,] 9.5239994 4.5037660
## [176,] 10.2570975 3.7706679
## [177,] 11.3327143 2.6950511
## [178,] 11.6878565 2.3399089
## [179,] 11.6878565 2.3399089
## [180,] 11.6095943 2.4181711
## [181,] 11.8775494 2.1502160
## [182,] 10.6225893 3.4051761
## [183,] 6.2969887 7.7307767
## [184,] 11.3327143 2.6950511
## [185,] 12.2326916 1.7950738
## [186,] 11.8775494 2.1502160
## [187,] 11.8775494 2.1502160
## [188,] 11.8775494 2.1502160
## [189,] 12.8638946 1.1638709
## [190,] 11.6878565 2.3399089
## [191,] 11.6878565 2.3399089
## [192,] 12.2326916 1.7950738
## [193,] 11.6878565 2.3399089
## [194,] 11.3327143 2.6950511
## [195,] 11.3327143 2.6950511
## [196,] 12.2326916 1.7950738
## [197,] 11.3327143 2.6950511
## [198,] 11.3327143 2.6950511
## [199,] 10.8609616 3.1668038
## [200,] 8.3666897 5.6610757
## [201,] 11.6878565 2.3399089
## [202,] 12.2326916 1.7950738
## [203,] 10.7057141 3.3220514
## [204,] 9.9558220 4.0719435
## [205,] 11.6878565 2.3399089
## [206,] 12.2326916 1.7950738
## [207,] 6.3721448 7.6556207
## [208,] 11.8775494 2.1502160
## [209,] 11.6878565 2.3399089
## [210,] 10.3921564 3.6356090
## [211,] 11.3327143 2.6950511
## [212,] 6.0956367 7.9321287
## [213,] 11.3327143 2.6950511
## [214,] 11.3327143 2.6950511
## [215,] 11.6878565 2.3399089
## [216,] 11.3327143 2.6950511
## [217,] 9.3268892 4.7008762
## [218,] 11.6878565 2.3399089
## [219,] 11.3327143 2.6950511
## [220,] 11.1368862 2.8908792
## [221,] 11.3327143 2.6950511
## [222,] 11.1328196 2.8949458
## [223,] 11.3327143 2.6950511
## [224,] 11.2505492 2.7772163
## [225,] 11.8775494 2.1502160
## [226,] 11.8775494 2.1502160
## [227,] 9.2395501 4.7882153
## [228,] 12.2326916 1.7950738
## [229,] 12.8638946 1.1638709
## [230,] 11.8775494 2.1502160
## [231,] 11.3327143 2.6950511
## [232,] 12.2326916 1.7950738
## [233,] 11.6878565 2.3399089
## [234,] 12.2326916 1.7950738
## [235,] 12.2326916 1.7950738
## [236,] 10.3109642 3.7168012
## [237,] 10.7057141 3.3220514
## [238,] 11.8290499 2.1987156
## [239,] 11.6056914 2.4220740
## [240,] 11.8775494 2.1502160
## [241,] 12.8638946 1.1638709
## [242,] 11.6878565 2.3399089
## [243,] 11.4920285 2.5357370
## [244,] 11.6878565 2.3399089
## [245,] 11.6878565 2.3399089
## [246,] 11.5966079 2.4311576
## [247,] 12.2326916 1.7950738
## [248,] 12.2326916 1.7950738
## [249,] 11.6878565 2.3399089
## [250,] 10.5879845 3.4397809
## [251,] 10.9933401 3.0344253
## [252,] 11.2842148 2.7435507
## [253,] 9.3704063 4.6573591
## [254,] 8.9044092 5.1233562
## [255,] 12.2326916 1.7950738
## [256,] 11.3327143 2.6950511
## [257,] 11.6878565 2.3399089
## [258,] 11.8775494 2.1502160
## [259,] 11.6878565 2.3399089
## [260,] 11.3327143 2.6950511
## [261,] 11.3327143 2.6950511
## [262,] 11.8775494 2.1502160
## [263,] 11.3327143 2.6950511
## [264,] 11.0608563 2.9669091
## [265,] 11.1368862 2.8908792
## [266,] 12.2368943 1.7908711
## [267,] 11.6878565 2.3399089
## [268,] 11.8775494 2.1502160
## [269,] 11.2505492 2.7772163
## [270,] 10.9290725 3.0986929
## [271,] 11.6878565 2.3399089
## [272,] 11.3327143 2.6950511
## [273,] 10.9431267 3.0846387
## [274,] 11.4879618 2.5398036
## [275,] 11.6878565 2.3399089
## [276,] 11.3327143 2.6950511
## [277,] 11.6878565 2.3399089
## [278,] 11.0608563 2.9669091
## [279,] 13.2190368 0.8087287
## [280,] 11.6878565 2.3399089
## [281,] 11.6878565 2.3399089
## [282,] 9.2595515 4.7682140
## [283,] 11.8775494 2.1502160
## [284,] 11.3327143 2.6950511
## [285,] 11.3327143 2.6950511
## [286,] 7.5056075 6.5221579
## [287,] 11.3327143 2.6950511
## [288,] 12.2326916 1.7950738
## [289,] 11.6878565 2.3399089
## [290,] 11.6878565 2.3399089
## [291,] 11.8775494 2.1502160
## [292,] 11.6878565 2.3399089
## [293,] 11.3327143 2.6950511
## [294,] 11.6878565 2.3399089
## [295,] 9.8791416 4.1486238
## [296,] 11.8775494 2.1502160
## [297,] 9.3805981 4.6471673
## [298,] 11.8775494 2.1502160
## [299,] 7.5849520 6.4428135
## [300,] 12.2326916 1.7950738
## [301,] 10.5879845 3.4397809
## [302,] 11.6878565 2.3399089
## [303,] 11.6878565 2.3399089
## [304,] 11.6056914 2.4220740
## [305,] 11.1674244 2.8603410
## [306,] 11.3327143 2.6950511
## [307,] 10.3921564 3.6356090
## [308,] 12.2326916 1.7950738
## [309,] 11.3327143 2.6950511
## [310,] 11.1674244 2.8603410
## [311,] 11.3327143 2.6950511
## [312,] 8.5834415 5.4443239
## [313,] 11.6817213 2.3460441
## [314,] 8.8969992 5.1307663
## [315,] 12.2326916 1.7950738
## [316,] 11.6056914 2.4220740
## [317,] 10.7831345 3.2446310
## [318,] 11.1368862 2.8908792
## [319,] 11.6817213 2.3460441
## [320,] 11.2505492 2.7772163
## [321,] 11.3327143 2.6950511
## [322,] 12.2326916 1.7950738
## [323,] 11.6878565 2.3399089
## [324,] 11.6878565 2.3399089
## [325,] 11.2008851 2.8268804
## [326,] 11.3327143 2.6950511
## [327,] 11.6878565 2.3399089
## [328,] 11.3327143 2.6950511
## [329,] 11.3327143 2.6950511
## [330,] 11.8775494 2.1502160
## [331,] 9.9558220 4.0719435
## [332,] 11.3327143 2.6950511
## [333,] 6.5679663 7.4597991
## [334,] 11.8775494 2.1502160
## [335,] 11.3327143 2.6950511
## [336,] 11.8775494 2.1502160
## [337,] 11.6878565 2.3399089
## [338,] 11.9233367 2.1044288
## [339,] 11.8775494 2.1502160
## [340,] 11.3327143 2.6950511
## [341,] 11.1368862 2.8908792
## [342,] 11.8775494 2.1502160
## [343,] 11.1368862 2.8908792
## [344,] 11.6878565 2.3399089
## [345,] 8.9251800 5.1025855
## [346,] 11.3327143 2.6950511
## [347,] 11.4920285 2.5357370
## [348,] 10.5333497 3.4944157
## [349,] 11.3327143 2.6950511
## [350,] 9.5239994 4.5037660
## [351,] 11.8775494 2.1502160
## [352,] 11.5966079 2.4311576
## [353,] 12.8638946 1.1638709
## [354,] 11.6878565 2.3399089
## [355,] 9.9609843 4.0667812
## [356,] 2.5970796 11.4306859
## [357,] 3.7319093 10.2958561
## [358,] 1.5812660 12.4464995
## [359,] 6.0510198 7.9767457
## [360,] 5.0383470 8.9894185
## [361,] 4.5281253 9.4996401
## [362,] 3.9799188 10.0478467
## [363,] 3.8416087 10.1861568
## [364,] 3.2772596 10.7505058
## [365,] 3.0999505 10.9278149
## [366,] 3.8363938 10.1913716
## [367,] 5.3528808 8.6748847
## [368,] 0.9312153 13.0965502
## [369,] 7.5935892 6.4341762
## [370,] 4.7253338 9.3024317
## [371,] 2.0602139 11.9675516
## [372,] 2.3382610 11.6895044
## [373,] 2.3903908 11.6373746
## [374,] 3.5877220 10.4400435
## [375,] 3.6780226 10.3497428
## [376,] 4.4092650 9.6185004
## [377,] 4.1701074 9.8576581
## [378,] 0.5402243 13.4875412
## [379,] 4.8485873 9.1791782
## [380,] 3.9980191 10.0297463
## [381,] 2.3029054 11.7248600
## [382,] 2.4707027 11.5570627
## [383,] 2.4604971 11.5672683
## [384,] 1.2370605 12.7907049
## [385,] 0.0000000 14.0277654
## [386,] 3.1769090 10.8508564
## [387,] 6.1305374 7.8972281
## [388,] 4.9007086 9.1270568
## [389,] 1.0534513 12.9743141
## [390,] 2.9804638 11.0473016
## [391,] 2.9731402 11.0546253
## [392,] 5.1030414 8.9247240
## [393,] 3.1125076 10.9152578
## [394,] 5.4715616 8.5562039
## [395,] 4.4421241 9.5856413
## [396,] 2.3442554 11.6835100
## [397,] 5.8113449 8.2164206
## [398,] 4.9007086 9.1270568
## [399,] 4.4092650 9.6185004
## [400,] 3.9843228 10.0434426
## [401,] 2.9809188 11.0468466
## [402,] 3.5877220 10.4400435
## [403,] 8.3595741 5.6681913
## [404,] 6.3280704 7.6996950
## [405,] 2.1127826 11.9149829
## [406,] 4.6829518 9.3448136
## [407,] 3.9882297 10.0395358
## [408,] 4.6086992 9.4190662
## [409,] 4.5531355 9.4746299
## [410,] 4.2516734 9.7760921
## [411,] 2.1052909 11.9224745
## [412,] 2.9221174 11.1056480
## [413,] 3.1735512 10.8542142
## [414,] 1.0534513 12.9743141
## [415,] 3.6387837 10.3889818
## [416,] 4.7582977 9.2694677
## [417,] 2.4889922 11.5387732
## [418,] 4.1877176 9.8400479
## [419,] 3.0439978 10.9837676
## [420,] 1.9487680 12.0789975
## [421,] 2.8799832 11.1477822
## [422,] 4.2598520 9.7679134
## [423,] 2.2404645 11.7873010
## [424,] 0.4279082 13.5998573
## [425,] 3.5681274 10.4596380
## [426,] 3.5335430 10.4942224
## [427,] 2.0602139 11.9675516
## [428,] 5.8113449 8.2164206
## [429,] 5.4652341 8.5625313
## [430,] 0.5595020 13.4682634
## [431,] 2.9731402 11.0546253
## [432,] 4.0172969 10.0104685
## [433,] 2.8634122 11.1643532
## [434,] 4.0567449 9.9710205
## [435,] 4.3875096 9.6402558
## [436,] 1.9487680 12.0789975
## [437,] 2.1127826 11.9149829
## [438,] 3.3423514 10.6854140
## [439,] 3.3764012 10.6513643
## [440,] 4.4738999 9.5538655
## [441,] 5.8873206 8.1404448
## [442,] 0.8894367 13.1383287
## [443,] 2.8441344 11.1836310
## [444,] 4.4602116 9.5675539
## [445,] 2.8920310 11.1357344
## [446,] 3.8601137 10.1676517
## [447,] 3.4887173 10.5390482
## [448,] 0.1640146 13.8637508
## [449,] 3.5335430 10.4942224
## [450,] 6.5919906 7.4357748
## [451,] 1.4070457 12.6207197
## [452,] 2.4352326 11.5925329
## [453,] 1.6132702 12.4144953
## [454,] 1.9487680 12.0789975
## [455,] 3.8625769 10.1651885
## [456,] 5.4997701 8.5279953
## [457,] 2.2242284 11.8035370
## [458,] 2.1127826 11.9149829
## [459,] 2.0394617 11.9883037
## [460,] 4.4813581 9.5464073
## [461,] 4.4634440 9.5643214
## [462,] 2.5082700 11.5194954
## [463,] 6.1338775 7.8938879
## [464,] 3.3558137 10.6719517
## [465,] 6.0904763 7.9372892
## [466,] 3.8511097 10.1766558
## [467,] 2.0764499 11.9513156
## [468,] 5.2223366 8.8054288
## [469,] 1.9487680 12.0789975
## [470,] 1.6067581 12.4210073
## [471,] 3.3991401 10.6286254
## [472,] 1.9849077 12.0428577
## [473,] 3.4360394 10.5917260
## [474,] 5.1643752 8.8633902
## [475,] 2.3766761 11.6510893
## [476,] 7.1829205 6.8448450
## [477,] 3.3051081 10.7226573
## [478,] 6.0535345 7.9742310
## [479,] 3.8551429 10.1726225
## [480,] 7.1770462 6.8507193
## [481,] 5.9395065 8.0882590
## [482,] 1.4010153 12.6267501
## [483,] 1.2370605 12.7907049
## [484,] 6.8238295 7.2039359
## [485,] 4.3977152 9.6300502
## [486,] 3.1599348 10.8678306
## [487,] 3.0259088 11.0018566
## [488,] 1.0730459 12.9547195
## [489,] 0.8894367 13.1383287
## [490,] 2.5146761 11.5130894
## [491,] 5.4178907 8.6098748
## [492,] 3.1982220 10.8295434
## [493,] 5.2341525 8.7936129
## [494,] 4.0343702 9.9933953
## [495,] 3.5821816 10.4455838
## [496,] 7.0020138 7.0257517
## [497,] 3.2276978 10.8000677
## [498,] 2.7305713 11.2971941
## [499,] 3.6582048 10.3695606
## [500,] 2.1127826 11.9149829
## [501,] 4.0333135 9.9944519
## [502,] 2.2964825 11.7312829
## [503,] 0.5595020 13.4682634
## [504,] 2.5067402 11.5210253
## [505,] 4.9029492 9.1248162
## [506,] 4.4634440 9.5643214
## [507,] 7.4141254 6.6136400
## [508,] 0.0000000 14.0277654
## [509,] 5.0203805 9.0073849
## [510,] 3.1222971 10.9054684
## [511,] 4.8190435 9.2087219
## [512,] 4.6411733 9.3865921
## [513,] 2.9521719 11.0755935
## [514,] 4.2180735 9.8096920
## [515,] 1.0730459 12.9547195
## [516,] 1.5009541 12.5268114
## [517,] 3.0999505 10.9278149
## [518,] 4.8185862 9.2091792
## [519,] 5.5662211 8.4615443
## [520,] 1.8928407 12.1349247
## [521,] 2.8743445 11.1534210
## [522,] 0.0000000 14.0277654
## [523,] 5.3656480 8.6621175
## [524,] 2.4707027 11.5570627
## [525,] 3.8416087 10.1861568
## [526,] 1.0034040 13.0243615
## [527,] 2.8027346 11.2250309
## [528,] 3.8379297 10.1898357
## [529,] 7.7248641 6.3029014
## [530,] 2.1127826 11.9149829
## [531,] 2.9382260 11.0895394
## [532,] 2.8295187 11.1982467
## [533,] 6.9231085 7.1046569
## [534,] 2.2488013 11.7789641
## [535,] 4.0702582 9.9575073
## [536,] 4.2735667 9.7541988
## [537,] 3.0999505 10.9278149
## [538,] 6.2789769 7.7487885
## [539,] 4.5458475 9.4819179
## [540,] 2.1264972 11.9012682
## [541,] 6.2365018 7.7912637
## [542,] 4.8776664 9.1500990
## [543,] 1.5710603 12.4567051
## [544,] 5.8430986 8.1846668
## [545,] 4.4092650 9.6185004
## [546,] 2.1112846 11.9164808
##
## $prob
## [,1] [,2]
## [1,] 0.80202005 0.19797995
## [2,] 0.91703091 0.08296909
## [3,] 0.87203423 0.12796577
## [4,] 0.87203423 0.12796577
## [5,] 0.80787737 0.19212263
## [6,] 0.74082764 0.25917236
## [7,] 0.83319447 0.16680553
## [8,] 0.84671714 0.15328286
## [9,] 0.80787737 0.19212263
## [10,] 0.80787737 0.19212263
## [11,] 0.91703091 0.08296909
## [12,] 0.87203423 0.12796577
## [13,] 0.84671714 0.15328286
## [14,] 0.71009059 0.28990941
## [15,] 0.79609432 0.20390568
## [16,] 0.87203423 0.12796577
## [17,] 0.52837245 0.47162755
## [18,] 0.65663289 0.34336711
## [19,] 0.87203423 0.12796577
## [20,] 0.80787737 0.19212263
## [21,] 0.84671714 0.15328286
## [22,] 0.80787737 0.19212263
## [23,] 0.79362744 0.20637256
## [24,] 0.87203423 0.12796577
## [25,] 0.74447193 0.25552807
## [26,] 0.78849738 0.21150262
## [27,] 0.83319447 0.16680553
## [28,] 0.79391734 0.20608266
## [29,] 0.83319447 0.16680553
## [30,] 0.80787737 0.19212263
## [31,] 0.80787737 0.19212263
## [32,] 0.80787737 0.19212263
## [33,] 0.80787737 0.19212263
## [34,] 0.78010477 0.21989523
## [35,] 0.84671714 0.15328286
## [36,] 0.81887613 0.18112387
## [37,] 0.87203423 0.12796577
## [38,] 0.84671714 0.15328286
## [39,] 0.77077723 0.22922277
## [40,] 0.80787737 0.19212263
## [41,] 0.80787737 0.19212263
## [42,] 0.91703091 0.08296909
## [43,] 0.80787737 0.19212263
## [44,] 0.74893036 0.25106964
## [45,] 0.84671714 0.15328286
## [46,] 0.87203423 0.12796577
## [47,] 0.91703091 0.08296909
## [48,] 0.94234801 0.05765199
## [49,] 0.83275710 0.16724290
## [50,] 0.83319447 0.16680553
## [51,] 0.80787737 0.19212263
## [52,] 0.50308177 0.49691823
## [53,] 0.80787737 0.19212263
## [54,] 0.80787737 0.19212263
## [55,] 0.80787737 0.19212263
## [56,] 0.83319447 0.16680553
## [57,] 0.83319447 0.16680553
## [58,] 0.83275710 0.16724290
## [59,] 0.84671714 0.15328286
## [60,] 0.42795521 0.57204479
## [61,] 0.61111084 0.38888916
## [62,] 0.83319447 0.16680553
## [63,] 0.87203423 0.12796577
## [64,] 0.81887613 0.18112387
## [65,] 0.95587068 0.04412932
## [66,] 0.83319447 0.16680553
## [67,] 0.87203423 0.12796577
## [68,] 0.76318029 0.23681971
## [69,] 0.77966741 0.22033259
## [70,] 0.70526786 0.29473214
## [71,] 0.79609432 0.20390568
## [72,] 0.83319447 0.16680553
## [73,] 0.80787737 0.19212263
## [74,] 0.83319447 0.16680553
## [75,] 0.50216442 0.49783558
## [76,] 0.71009059 0.28990941
## [77,] 0.80787737 0.19212263
## [78,] 0.80787737 0.19212263
## [79,] 0.84671714 0.15328286
## [80,] 0.91703091 0.08296909
## [81,] 0.77063863 0.22936137
## [82,] 0.80787737 0.19212263
## [83,] 0.73193746 0.26806254
## [84,] 0.80787737 0.19212263
## [85,] 0.80787737 0.19212263
## [86,] 0.61193580 0.38806420
## [87,] 0.80787737 0.19212263
## [88,] 0.80787737 0.19212263
## [89,] 0.84671714 0.15328286
## [90,] 0.83319447 0.16680553
## [91,] 0.87203423 0.12796577
## [92,] 0.80786737 0.19213263
## [93,] 0.87203423 0.12796577
## [94,] 0.66008742 0.33991258
## [95,] 0.84671714 0.15328286
## [96,] 0.55473782 0.44526218
## [97,] 0.87203423 0.12796577
## [98,] 0.84671714 0.15328286
## [99,] 0.83319447 0.16680553
## [100,] 0.68131610 0.31868390
## [101,] 0.83319447 0.16680553
## [102,] 0.83319447 0.16680553
## [103,] 0.79391734 0.20608266
## [104,] 0.73540768 0.26459232
## [105,] 0.77966741 0.22033259
## [106,] 0.80787737 0.19212263
## [107,] 0.80787737 0.19212263
## [108,] 0.77966741 0.22033259
## [109,] 0.84671714 0.15328286
## [110,] 0.51856508 0.48143492
## [111,] 0.84671714 0.15328286
## [112,] 0.80787737 0.19212263
## [113,] 0.92809807 0.07190193
## [114,] 0.87203423 0.12796577
## [115,] 0.84671714 0.15328286
## [116,] 0.87203423 0.12796577
## [117,] 0.64884571 0.35115429
## [118,] 0.80787737 0.19212263
## [119,] 0.80787737 0.19212263
## [120,] 0.87203423 0.12796577
## [121,] 0.49832970 0.50167030
## [122,] 0.87203423 0.12796577
## [123,] 0.79391734 0.20608266
## [124,] 0.84671714 0.15328286
## [125,] 0.83319447 0.16680553
## [126,] 0.80787737 0.19212263
## [127,] 0.87203423 0.12796577
## [128,] 0.79391734 0.20608266
## [129,] 0.87203423 0.12796577
## [130,] 0.80787737 0.19212263
## [131,] 0.78849738 0.21150262
## [132,] 0.83319447 0.16680553
## [133,] 0.80787737 0.19212263
## [134,] 0.84671714 0.15328286
## [135,] 0.85200670 0.14799330
## [136,] 0.73576622 0.26423378
## [137,] 0.76318029 0.23681971
## [138,] 0.79391734 0.20608266
## [139,] 0.84671714 0.15328286
## [140,] 0.80787737 0.19212263
## [141,] 0.73503968 0.26496032
## [142,] 0.84671714 0.15328286
## [143,] 0.80787737 0.19212263
## [144,] 0.87203423 0.12796577
## [145,] 0.83275710 0.16724290
## [146,] 0.80498450 0.19501550
## [147,] 0.87203423 0.12796577
## [148,] 0.91703091 0.08296909
## [149,] 0.83319447 0.16680553
## [150,] 0.87203423 0.12796577
## [151,] 0.80787737 0.19212263
## [152,] 0.84671714 0.15328286
## [153,] 0.80787737 0.19212263
## [154,] 0.84671714 0.15328286
## [155,] 0.80787737 0.19212263
## [156,] 0.80787737 0.19212263
## [157,] 0.78331169 0.21668831
## [158,] 0.87203423 0.12796577
## [159,] 0.62124765 0.37875235
## [160,] 0.83319447 0.16680553
## [161,] 0.74893036 0.25106964
## [162,] 0.88882095 0.11117905
## [163,] 0.78849738 0.21150262
## [164,] 0.83319447 0.16680553
## [165,] 0.83319447 0.16680553
## [166,] 0.80787737 0.19212263
## [167,] 0.79362744 0.20637256
## [168,] 0.73193746 0.26806254
## [169,] 0.83319447 0.16680553
## [170,] 0.83319447 0.16680553
## [171,] 0.76318029 0.23681971
## [172,] 0.87203423 0.12796577
## [173,] 0.80787737 0.19212263
## [174,] 0.87203423 0.12796577
## [175,] 0.67893917 0.32106083
## [176,] 0.73119967 0.26880033
## [177,] 0.80787737 0.19212263
## [178,] 0.83319447 0.16680553
## [179,] 0.83319447 0.16680553
## [180,] 0.82761537 0.17238463
## [181,] 0.84671714 0.15328286
## [182,] 0.75725456 0.24274544
## [183,] 0.44889464 0.55110536
## [184,] 0.80787737 0.19212263
## [185,] 0.87203423 0.12796577
## [186,] 0.84671714 0.15328286
## [187,] 0.84671714 0.15328286
## [188,] 0.84671714 0.15328286
## [189,] 0.91703091 0.08296909
## [190,] 0.83319447 0.16680553
## [191,] 0.83319447 0.16680553
## [192,] 0.87203423 0.12796577
## [193,] 0.83319447 0.16680553
## [194,] 0.80787737 0.19212263
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## [433,] 0.20412462 0.79587538
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## [472,] 0.14149850 0.85850150
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## [481,] 0.42341073 0.57658927
## [482,] 0.09987445 0.90012555
## [483,] 0.08818657 0.91181343
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## [485,] 0.31350077 0.68649923
## [486,] 0.22526288 0.77473712
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## [492,] 0.22799226 0.77200774
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## [494,] 0.28759892 0.71240108
## [495,] 0.25536366 0.74463634
## [496,] 0.49915390 0.50084610
## [497,] 0.23009351 0.76990649
## [498,] 0.19465476 0.80534524
## [499,] 0.26078315 0.73921685
## [500,] 0.15061433 0.84938567
## [501,] 0.28752359 0.71247641
## [502,] 0.16370979 0.83629021
## [503,] 0.03988533 0.96011467
## [504,] 0.17869847 0.82130153
## [505,] 0.34951748 0.65048252
## [506,] 0.31818639 0.68181361
## [507,] 0.52853218 0.47146782
## [508,] 0.00000000 1.00000000
## [509,] 0.35788883 0.64211117
## [510,] 0.22257979 0.77742021
## [511,] 0.34353608 0.65646392
## [512,] 0.33085621 0.66914379
## [513,] 0.21045205 0.78954795
## [514,] 0.30069461 0.69930539
## [515,] 0.07649443 0.92350557
## [516,] 0.10699880 0.89300120
## [517,] 0.22098677 0.77901323
## [518,] 0.34350348 0.65649652
## [519,] 0.39680027 0.60319973
## [520,] 0.13493530 0.86506470
## [521,] 0.20490395 0.79509605
## [522,] 0.00000000 1.00000000
## [523,] 0.38250197 0.61749803
## [524,] 0.17612946 0.82387054
## [525,] 0.27385749 0.72614251
## [526,] 0.07152985 0.92847015
## [527,] 0.19979908 0.80020092
## [528,] 0.27359523 0.72640477
## [529,] 0.55068386 0.44931614
## [530,] 0.15061433 0.84938567
## [531,] 0.20945788 0.79054212
## [532,] 0.20170844 0.79829156
## [533,] 0.49352896 0.50647104
## [534,] 0.16031073 0.83968927
## [535,] 0.29015727 0.70984273
## [536,] 0.30465057 0.69534943
## [537,] 0.22098677 0.77901323
## [538,] 0.44761063 0.55238937
## [539,] 0.32406070 0.67593930
## [540,] 0.15159202 0.84840798
## [541,] 0.44458269 0.55541731
## [542,] 0.34771514 0.65228486
## [543,] 0.11199648 0.88800352
## [544,] 0.41653809 0.58346191
## [545,] 0.31432412 0.68567588
## [546,] 0.15050755 0.84949245
##
## $class
## [1] "benign" "benign" "benign" "benign" "benign" "benign"
## [7] "benign" "benign" "benign" "benign" "benign" "benign"
## [13] "benign" "benign" "benign" "benign" "benign" "benign"
## [19] "benign" "benign" "benign" "benign" "benign" "benign"
## [25] "benign" "benign" "benign" "benign" "benign" "benign"
## [31] "benign" "benign" "benign" "benign" "benign" "benign"
## [37] "benign" "benign" "benign" "benign" "benign" "benign"
## [43] "benign" "benign" "benign" "benign" "benign" "benign"
## [49] "benign" "benign" "benign" "benign" "benign" "benign"
## [55] "benign" "benign" "benign" "benign" "benign" "malignant"
## [61] "benign" "benign" "benign" "benign" "benign" "benign"
## [67] "benign" "benign" "benign" "benign" "benign" "benign"
## [73] "benign" "benign" "benign" "benign" "benign" "benign"
## [79] "benign" "benign" "benign" "benign" "benign" "benign"
## [85] "benign" "benign" "benign" "benign" "benign" "benign"
## [91] "benign" "benign" "benign" "benign" "benign" "benign"
## [97] "benign" "benign" "benign" "benign" "benign" "benign"
## [103] "benign" "benign" "benign" "benign" "benign" "benign"
## [109] "benign" "benign" "benign" "benign" "benign" "benign"
## [115] "benign" "benign" "benign" "benign" "benign" "benign"
## [121] "malignant" "benign" "benign" "benign" "benign" "benign"
## [127] "benign" "benign" "benign" "benign" "benign" "benign"
## [133] "benign" "benign" "benign" "benign" "benign" "benign"
## [139] "benign" "benign" "benign" "benign" "benign" "benign"
## [145] "benign" "benign" "benign" "benign" "benign" "benign"
## [151] "benign" "benign" "benign" "benign" "benign" "benign"
## [157] "benign" "benign" "benign" "benign" "benign" "benign"
## [163] "benign" "benign" "benign" "benign" "benign" "benign"
## [169] "benign" "benign" "benign" "benign" "benign" "benign"
## [175] "benign" "benign" "benign" "benign" "benign" "benign"
## [181] "benign" "benign" "malignant" "benign" "benign" "benign"
## [187] "benign" "benign" "benign" "benign" "benign" "benign"
## [193] "benign" "benign" "benign" "benign" "benign" "benign"
## [199] "benign" "benign" "benign" "benign" "benign" "benign"
## [205] "benign" "benign" "malignant" "benign" "benign" "benign"
## [211] "benign" "malignant" "benign" "benign" "benign" "benign"
## [217] "benign" "benign" "benign" "benign" "benign" "benign"
## [223] "benign" "benign" "benign" "benign" "benign" "benign"
## [229] "benign" "benign" "benign" "benign" "benign" "benign"
## [235] "benign" "benign" "benign" "benign" "benign" "benign"
## [241] "benign" "benign" "benign" "benign" "benign" "benign"
## [247] "benign" "benign" "benign" "benign" "benign" "benign"
## [253] "benign" "benign" "benign" "benign" "benign" "benign"
## [259] "benign" "benign" "benign" "benign" "benign" "benign"
## [265] "benign" "benign" "benign" "benign" "benign" "benign"
## [271] "benign" "benign" "benign" "benign" "benign" "benign"
## [277] "benign" "benign" "benign" "benign" "benign" "benign"
## [283] "benign" "benign" "benign" "benign" "benign" "benign"
## [289] "benign" "benign" "benign" "benign" "benign" "benign"
## [295] "benign" "benign" "benign" "benign" "benign" "benign"
## [301] "benign" "benign" "benign" "benign" "benign" "benign"
## [307] "benign" "benign" "benign" "benign" "benign" "benign"
## [313] "benign" "benign" "benign" "benign" "benign" "benign"
## [319] "benign" "benign" "benign" "benign" "benign" "benign"
## [325] "benign" "benign" "benign" "benign" "benign" "benign"
## [331] "benign" "benign" "malignant" "benign" "benign" "benign"
## [337] "benign" "benign" "benign" "benign" "benign" "benign"
## [343] "benign" "benign" "benign" "benign" "benign" "benign"
## [349] "benign" "benign" "benign" "benign" "benign" "benign"
## [355] "benign" "malignant" "malignant" "malignant" "malignant" "malignant"
## [361] "malignant" "malignant" "malignant" "malignant" "malignant" "malignant"
## [367] "malignant" "malignant" "benign" "malignant" "malignant" "malignant"
## [373] "malignant" "malignant" "malignant" "malignant" "malignant" "malignant"
## [379] "malignant" "malignant" "malignant" "malignant" "malignant" "malignant"
## [385] "malignant" "malignant" "malignant" "malignant" "malignant" "malignant"
## [391] "malignant" "malignant" "malignant" "malignant" "malignant" "malignant"
## [397] "malignant" "malignant" "malignant" "malignant" "malignant" "malignant"
## [403] "benign" "malignant" "malignant" "malignant" "malignant" "malignant"
## [409] "malignant" "malignant" "malignant" "malignant" "malignant" "malignant"
## [415] "malignant" "malignant" "malignant" "malignant" "malignant" "malignant"
## [421] "malignant" "malignant" "malignant" "malignant" "malignant" "malignant"
## [427] "malignant" "malignant" "malignant" "malignant" "malignant" "malignant"
## [433] "malignant" "malignant" "malignant" "malignant" "malignant" "malignant"
## [439] "malignant" "malignant" "malignant" "malignant" "malignant" "malignant"
## [445] "malignant" "malignant" "malignant" "malignant" "malignant" "malignant"
## [451] "malignant" "malignant" "malignant" "malignant" "malignant" "malignant"
## [457] "malignant" "malignant" "malignant" "malignant" "malignant" "malignant"
## [463] "malignant" "malignant" "malignant" "malignant" "malignant" "malignant"
## [469] "malignant" "malignant" "malignant" "malignant" "malignant" "malignant"
## [475] "malignant" "benign" "malignant" "malignant" "malignant" "benign"
## [481] "malignant" "malignant" "malignant" "malignant" "malignant" "malignant"
## [487] "malignant" "malignant" "malignant" "malignant" "malignant" "malignant"
## [493] "malignant" "malignant" "malignant" "malignant" "malignant" "malignant"
## [499] "malignant" "malignant" "malignant" "malignant" "malignant" "malignant"
## [505] "malignant" "malignant" "benign" "malignant" "malignant" "malignant"
## [511] "malignant" "malignant" "malignant" "malignant" "malignant" "malignant"
## [517] "malignant" "malignant" "malignant" "malignant" "malignant" "malignant"
## [523] "malignant" "malignant" "malignant" "malignant" "malignant" "malignant"
## [529] "benign" "malignant" "malignant" "malignant" "malignant" "malignant"
## [535] "malignant" "malignant" "malignant" "malignant" "malignant" "malignant"
## [541] "malignant" "malignant" "malignant" "malignant" "malignant" "malignant"
##
## $importance
## Bare.nuclei Bl.cromatin Cell.shape Cell.size Cl.thickness
## 28.6419299 6.9671513 2.7165269 33.3839482 12.9229544
## Epith.c.size Marg.adhesion Mitoses Normal.nucleoli
## 0.3902495 3.2281815 0.6881028 11.0609555
##
## $terms
## Class ~ Cl.thickness + Cell.size + Cell.shape + Marg.adhesion +
## Epith.c.size + Bare.nuclei + Bl.cromatin + Normal.nucleoli +
## Mitoses
## attr(,"variables")
## list(Class, Cl.thickness, Cell.size, Cell.shape, Marg.adhesion,
## Epith.c.size, Bare.nuclei, Bl.cromatin, Normal.nucleoli,
## Mitoses)
## attr(,"factors")
## Cl.thickness Cell.size Cell.shape Marg.adhesion Epith.c.size
## Class 0 0 0 0 0
## Cl.thickness 1 0 0 0 0
## Cell.size 0 1 0 0 0
## Cell.shape 0 0 1 0 0
## Marg.adhesion 0 0 0 1 0
## Epith.c.size 0 0 0 0 1
## Bare.nuclei 0 0 0 0 0
## Bl.cromatin 0 0 0 0 0
## Normal.nucleoli 0 0 0 0 0
## Mitoses 0 0 0 0 0
## Bare.nuclei Bl.cromatin Normal.nucleoli Mitoses
## Class 0 0 0 0
## Cl.thickness 0 0 0 0
## Cell.size 0 0 0 0
## Cell.shape 0 0 0 0
## Marg.adhesion 0 0 0 0
## Epith.c.size 0 0 0 0
## Bare.nuclei 1 0 0 0
## Bl.cromatin 0 1 0 0
## Normal.nucleoli 0 0 1 0
## Mitoses 0 0 0 1
## attr(,"term.labels")
## [1] "Cl.thickness" "Cell.size" "Cell.shape" "Marg.adhesion"
## [5] "Epith.c.size" "Bare.nuclei" "Bl.cromatin" "Normal.nucleoli"
## [9] "Mitoses"
## attr(,"order")
## [1] 1 1 1 1 1 1 1 1 1
## attr(,"intercept")
## [1] 1
## attr(,"response")
## [1] 1
## attr(,".Environment")
## <environment: R_GlobalEnv>
## attr(,"predvars")
## list(Class, Cl.thickness, Cell.size, Cell.shape, Marg.adhesion,
## Epith.c.size, Bare.nuclei, Bl.cromatin, Normal.nucleoli,
## Mitoses)
## attr(,"dataClasses")
## Class Cl.thickness Cell.size Cell.shape Marg.adhesion
## "factor" "ordered" "ordered" "ordered" "ordered"
## Epith.c.size Bare.nuclei Bl.cromatin Normal.nucleoli Mitoses
## "ordered" "factor" "factor" "factor" "factor"
##
## $call
## adabag::boosting(formula = Class ~ ., data = data_train, mfinal = 50,
## control = rpart::rpart.control(maxdepth = 1, minsplit = 2,
## cp = 0))
##
## attr(,"vardep.summary")
## benign malignant
## 355 191
## attr(,"class")
## [1] "boosting"Model kedua menggunakan 100 learner. Model ini digunakan untuk mengetahui perubahan performa klasifikasi ketika jumlah learner ditingkatkan menjadi dua kali jumlah pada model pertama.
set.seed(22)
class_mod100 <- adabag::boosting(
Class ~ .,
data = data_train,
mfinal = 100,
control = rpart::rpart.control(
maxdepth = 1,
minsplit = 2,
cp = 0
)
)
print(class_mod100)## $formula
## Class ~ .
##
## $trees
## $trees[[1]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 182 benign (0.66666667 0.33333333)
## 2) Cell.size=1,2,3 383 25 benign (0.93472585 0.06527415) *
## 3) Cell.size=4,5,6,7,8,9,10 163 6 malignant (0.03680982 0.96319018) *
##
## $trees[[2]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 220 benign (0.59706960 0.40293040)
## 2) Bare.nuclei=1,2,3 336 29 benign (0.91369048 0.08630952) *
## 3) Bare.nuclei=4,5,6,7,8,9,10 210 19 malignant (0.09047619 0.90952381) *
##
## $trees[[3]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 238 benign (0.56410256 0.43589744)
## 2) Cell.size=1 225 6 benign (0.97333333 0.02666667) *
## 3) Cell.size=2,3,4,5,6,7,8,9,10 321 89 malignant (0.27725857 0.72274143) *
##
## $trees[[4]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 195 benign (0.6428571 0.3571429)
## 2) Cl.thickness=1,2,3,4,5,6 407 72 benign (0.8230958 0.1769042) *
## 3) Cl.thickness=7,8,9,10 139 16 malignant (0.1151079 0.8848921) *
##
## $trees[[5]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 232 benign (0.5750916 0.4249084)
## 2) Bare.nuclei=1,4,7,8 317 51 benign (0.8391167 0.1608833) *
## 3) Bare.nuclei=2,3,5,6,9,10 229 48 malignant (0.2096070 0.7903930) *
##
## $trees[[6]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 250 benign (0.5421245 0.4578755)
## 2) Normal.nucleoli=1,2,7,8 346 87 benign (0.7485549 0.2514451) *
## 3) Normal.nucleoli=3,4,5,6,9,10 200 37 malignant (0.1850000 0.8150000) *
##
## $trees[[7]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 271 malignant (0.4963370 0.5036630)
## 2) Bl.cromatin=1,2,3,4 361 110 benign (0.6952909 0.3047091) *
## 3) Bl.cromatin=5,6,7,8,9,10 185 20 malignant (0.1081081 0.8918919) *
##
## $trees[[8]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 249 malignant (0.4560440 0.5439560)
## 2) Cell.shape=1,2 179 41 benign (0.7709497 0.2290503) *
## 3) Cell.shape=3,4,5,6,7,8,9,10 367 111 malignant (0.3024523 0.6975477) *
##
## $trees[[9]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 270 benign (0.5054945 0.4945055)
## 2) Cl.thickness=1,2,3,4,5,6,7,8 460 184 benign (0.6000000 0.4000000) *
## 3) Cl.thickness=9,10 86 0 malignant (0.0000000 1.0000000) *
##
## $trees[[10]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 232 malignant (0.424908425 0.575091575)
## 2) Marg.adhesion=1,2,3,4,5 437 206 benign (0.528604119 0.471395881) *
## 3) Marg.adhesion=6,7,8,9,10 109 1 malignant (0.009174312 0.990825688) *
##
## $trees[[11]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 232 malignant (0.4249084 0.5750916)
## 2) Bare.nuclei=1,7 159 40 benign (0.7484277 0.2515723) *
## 3) Bare.nuclei=2,3,4,5,6,8,9,10 387 113 malignant (0.2919897 0.7080103) *
##
## $trees[[12]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 235 malignant (0.43040293 0.56959707)
## 2) Marg.adhesion=1,2,3,4,5 440 211 benign (0.52045455 0.47954545) *
## 3) Marg.adhesion=6,7,8,9,10 106 6 malignant (0.05660377 0.94339623) *
##
## $trees[[13]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 243 malignant (0.4450549 0.5549451)
## 2) Normal.nucleoli=2,3,7,8 148 39 benign (0.7364865 0.2635135) *
## 3) Normal.nucleoli=1,4,5,6,9,10 398 134 malignant (0.3366834 0.6633166) *
##
## $trees[[14]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 249 malignant (0.4560440 0.5439560)
## 2) Marg.adhesion=1,2,3,4,5 457 208 benign (0.5448578 0.4551422) *
## 3) Marg.adhesion=6,7,8,9,10 89 0 malignant (0.0000000 1.0000000) *
##
## $trees[[15]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 251 malignant (0.4597070 0.5402930)
## 2) Cl.thickness=1,2,3,4,5,6,7,8 470 219 benign (0.5340426 0.4659574) *
## 3) Cl.thickness=9,10 76 0 malignant (0.0000000 1.0000000) *
##
## $trees[[16]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 218 malignant (0.3992674 0.6007326)
## 2) Bl.cromatin=1,2,3,6 297 126 benign (0.5757576 0.4242424) *
## 3) Bl.cromatin=4,5,7,8,9,10 249 47 malignant (0.1887550 0.8112450) *
##
## $trees[[17]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 218 malignant (0.3992674 0.6007326)
## 2) Cl.thickness=1,2 70 7 benign (0.9000000 0.1000000) *
## 3) Cl.thickness=3,4,5,6,7,8,9,10 476 155 malignant (0.3256303 0.6743697) *
##
## $trees[[18]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 249 malignant (0.4560440 0.5439560)
## 2) Cl.thickness=1,2,3,4,5,6,7,8 473 224 benign (0.5264271 0.4735729) *
## 3) Cl.thickness=9,10 73 0 malignant (0.0000000 1.0000000) *
##
## $trees[[19]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 247 malignant (0.4523810 0.5476190)
## 2) Cl.thickness=1,2,3,4,5,6,7,8 481 234 benign (0.5135135 0.4864865) *
## 3) Cl.thickness=9,10 65 0 malignant (0.0000000 1.0000000) *
##
## $trees[[20]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 233 malignant (0.42673993 0.57326007)
## 2) Normal.nucleoli=1,2,4,5,6,7,8 440 216 benign (0.50909091 0.49090909) *
## 3) Normal.nucleoli=3,9,10 106 9 malignant (0.08490566 0.91509434) *
##
## $trees[[21]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 210 malignant (0.38461538 0.61538462)
## 2) Bl.cromatin=1,6 52 5 benign (0.90384615 0.09615385) *
## 3) Bl.cromatin=2,3,4,5,7,8,9,10 494 163 malignant (0.32995951 0.67004049) *
##
## $trees[[22]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 241 malignant (0.4413919 0.5586081)
## 2) Cell.shape=1 81 14 benign (0.8271605 0.1728395) *
## 3) Cell.shape=2,3,4,5,6,7,8,9,10 465 174 malignant (0.3741935 0.6258065) *
##
## $trees[[23]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 260 benign (0.52380952 0.47619048)
## 2) Marg.adhesion=1,2,3,4,5 485 203 benign (0.58144330 0.41855670) *
## 3) Marg.adhesion=6,7,8,9,10 61 4 malignant (0.06557377 0.93442623) *
##
## $trees[[24]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 240 malignant (0.4395604 0.5604396)
## 2) Bare.nuclei=1,3,7,8 265 103 benign (0.6113208 0.3886792) *
## 3) Bare.nuclei=2,4,5,6,9,10 281 78 malignant (0.2775801 0.7224199) *
##
## $trees[[25]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 237 malignant (0.4340659 0.5659341)
## 2) Cl.thickness=1,2,3,4,5,6,7,8 471 234 benign (0.5031847 0.4968153) *
## 3) Cl.thickness=9,10 75 0 malignant (0.0000000 1.0000000) *
##
## $trees[[26]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 231 malignant (0.4230769 0.5769231)
## 2) Normal.nucleoli=2,4,7 102 20 benign (0.8039216 0.1960784) *
## 3) Normal.nucleoli=1,3,5,6,8,9,10 444 149 malignant (0.3355856 0.6644144) *
##
## $trees[[27]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 245 malignant (0.4487179 0.5512821)
## 2) Cell.size=1,2,3,4 404 184 benign (0.5445545 0.4554455) *
## 3) Cell.size=5,6,7,8,9,10 142 25 malignant (0.1760563 0.8239437) *
##
## $trees[[28]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 260 malignant (0.4761905 0.5238095)
## 2) Normal.nucleoli=1,2,3,4,5,6,7,8 494 234 benign (0.5263158 0.4736842) *
## 3) Normal.nucleoli=9,10 52 0 malignant (0.0000000 1.0000000) *
##
## $trees[[29]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 220 malignant (0.4029304 0.5970696)
## 2) Bare.nuclei=1,2,3 168 64 benign (0.6190476 0.3809524) *
## 3) Bare.nuclei=4,5,6,7,8,9,10 378 116 malignant (0.3068783 0.6931217) *
##
## $trees[[30]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 254 malignant (0.4652015 0.5347985)
## 2) Normal.nucleoli=1,2,3,4,5,6,7,8 488 234 benign (0.5204918 0.4795082) *
## 3) Normal.nucleoli=9,10 58 0 malignant (0.0000000 1.0000000) *
##
## $trees[[31]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 240 malignant (0.43956044 0.56043956)
## 2) Normal.nucleoli=2 46 3 benign (0.93478261 0.06521739) *
## 3) Normal.nucleoli=1,3,4,5,6,7,8,9,10 500 197 malignant (0.39400000 0.60600000) *
##
## $trees[[32]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 248 malignant (0.4542125 0.5457875)
## 2) Cl.thickness=1,2,3,4,5,6,7,8 490 242 benign (0.5061224 0.4938776) *
## 3) Cl.thickness=9,10 56 0 malignant (0.0000000 1.0000000) *
##
## $trees[[33]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 264 malignant (0.4835165 0.5164835)
## 2) Normal.nucleoli=1,2,3,5,7,8 405 170 benign (0.5802469 0.4197531) *
## 3) Normal.nucleoli=4,6,9,10 141 29 malignant (0.2056738 0.7943262) *
##
## $trees[[34]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 256 malignant (0.46886447 0.53113553)
## 2) Marg.adhesion=1,2,3,4,5 486 234 benign (0.51851852 0.48148148) *
## 3) Marg.adhesion=6,7,8,9,10 60 4 malignant (0.06666667 0.93333333) *
##
## $trees[[35]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 229 malignant (0.4194139 0.5805861)
## 2) Normal.nucleoli=2,4,7 110 35 benign (0.6818182 0.3181818) *
## 3) Normal.nucleoli=1,3,5,6,8,9,10 436 154 malignant (0.3532110 0.6467890) *
##
## $trees[[36]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 249 malignant (0.4560440 0.5439560)
## 2) Cl.thickness=1,2,3,4,5,6,7,8 495 246 benign (0.5030303 0.4969697) *
## 3) Cl.thickness=9,10 51 0 malignant (0.0000000 1.0000000) *
##
## $trees[[37]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 237 malignant (0.4340659 0.5659341)
## 2) Mitoses=2 78 14 benign (0.8205128 0.1794872) *
## 3) Mitoses=1,3,4,6,7,8,10 468 173 malignant (0.3696581 0.6303419) *
##
## $trees[[38]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 245 malignant (0.4487179 0.5512821)
## 2) Cell.size=1,2,3,4 401 186 benign (0.5361596 0.4638404) *
## 3) Cell.size=5,6,7,8,9,10 145 30 malignant (0.2068966 0.7931034) *
##
## $trees[[39]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 235 malignant (0.4304029 0.5695971)
## 2) Normal.nucleoli=2,3,4,8 160 55 benign (0.6562500 0.3437500) *
## 3) Normal.nucleoli=1,5,6,7,9,10 386 130 malignant (0.3367876 0.6632124) *
##
## $trees[[40]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 251 benign (0.5402930 0.4597070)
## 2) Normal.nucleoli=1,2,3,4,5,6,7,8 505 210 benign (0.5841584 0.4158416) *
## 3) Normal.nucleoli=9,10 41 0 malignant (0.0000000 1.0000000) *
##
## $trees[[41]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 270 benign (0.5054945 0.4945055)
## 2) Cl.thickness=1,2,3,4,5,6,7,8 498 222 benign (0.5542169 0.4457831) *
## 3) Cl.thickness=9,10 48 0 malignant (0.0000000 1.0000000) *
##
## $trees[[42]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 253 malignant (0.4633700 0.5366300)
## 2) Cl.thickness=1,2,3,4,5,6 425 193 benign (0.5458824 0.4541176) *
## 3) Cl.thickness=7,8,9,10 121 21 malignant (0.1735537 0.8264463) *
##
## $trees[[43]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 248 malignant (0.45421245 0.54578755)
## 2) Marg.adhesion=1,2,3,4,5 480 237 benign (0.50625000 0.49375000) *
## 3) Marg.adhesion=6,7,8,9,10 66 5 malignant (0.07575758 0.92424242) *
##
## $trees[[44]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 226 malignant (0.4139194 0.5860806)
## 2) Bare.nuclei=1,2,3,7 198 81 benign (0.5909091 0.4090909) *
## 3) Bare.nuclei=4,5,6,8,9,10 348 109 malignant (0.3132184 0.6867816) *
##
## $trees[[45]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 226 malignant (0.41391941 0.58608059)
## 2) Bl.cromatin=1 36 3 benign (0.91666667 0.08333333) *
## 3) Bl.cromatin=2,3,4,5,6,7,8,10 510 193 malignant (0.37843137 0.62156863) *
##
## $trees[[46]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 258 malignant (0.4725275 0.5274725)
## 2) Epith.c.size=1,2,3,4,5 425 190 benign (0.5529412 0.4470588) *
## 3) Epith.c.size=6,7,8,9,10 121 23 malignant (0.1900826 0.8099174) *
##
## $trees[[47]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 245 malignant (0.4487179 0.5512821)
## 2) Normal.nucleoli=1,2,3,4,5,7,8 446 217 benign (0.5134529 0.4865471) *
## 3) Normal.nucleoli=6,9,10 100 16 malignant (0.1600000 0.8400000) *
##
## $trees[[48]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 250 malignant (0.4578755 0.5421245)
## 2) Normal.nucleoli=1,2,5,7,8 347 143 benign (0.5878963 0.4121037) *
## 3) Normal.nucleoli=3,4,6,9,10 199 46 malignant (0.2311558 0.7688442) *
##
## $trees[[49]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 244 malignant (0.4468864 0.5531136)
## 2) Cl.thickness=1,2,3,4,5,6,7,8 484 240 benign (0.5041322 0.4958678) *
## 3) Cl.thickness=9,10 62 0 malignant (0.0000000 1.0000000) *
##
## $trees[[50]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 235 malignant (0.4304029 0.5695971)
## 2) Normal.nucleoli=2,5,7 104 31 benign (0.7019231 0.2980769) *
## 3) Normal.nucleoli=1,3,4,6,8,9,10 442 162 malignant (0.3665158 0.6334842) *
##
## $trees[[51]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 259 malignant (0.4743590 0.5256410)
## 2) Cl.thickness=1,2,3,4,5,6,7,8 496 237 benign (0.5221774 0.4778226) *
## 3) Cl.thickness=9,10 50 0 malignant (0.0000000 1.0000000) *
##
## $trees[[52]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 240 malignant (0.4395604 0.5604396)
## 2) Marg.adhesion=1 204 81 benign (0.6029412 0.3970588) *
## 3) Marg.adhesion=2,3,4,5,6,7,8,9,10 342 117 malignant (0.3421053 0.6578947) *
##
## $trees[[53]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 249 malignant (0.4560440 0.5439560)
## 2) Bare.nuclei=1,2,3,8 250 98 benign (0.6080000 0.3920000) *
## 3) Bare.nuclei=4,5,6,7,9,10 296 97 malignant (0.3277027 0.6722973) *
##
## $trees[[54]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 266 malignant (0.4871795 0.5128205)
## 2) Cl.thickness=1,2,3,4,5,6,7,8 504 238 benign (0.5277778 0.4722222) *
## 3) Cl.thickness=9,10 42 0 malignant (0.0000000 1.0000000) *
##
## $trees[[55]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 267 malignant (0.4890110 0.5109890)
## 2) Cl.thickness=1,2,3,4,5,6,7,8 504 237 benign (0.5297619 0.4702381) *
## 3) Cl.thickness=9,10 42 0 malignant (0.0000000 1.0000000) *
##
## $trees[[56]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 244 malignant (0.4468864 0.5531136)
## 2) Mitoses=2 63 10 benign (0.8412698 0.1587302) *
## 3) Mitoses=1,3,4,5,8,10 483 191 malignant (0.3954451 0.6045549) *
##
## $trees[[57]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 266 malignant (0.4871795 0.5128205)
## 2) Normal.nucleoli=1,2,3,4,5,6,7,8 501 235 benign (0.5309381 0.4690619) *
## 3) Normal.nucleoli=9,10 45 0 malignant (0.0000000 1.0000000) *
##
## $trees[[58]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 259 malignant (0.4743590 0.5256410)
## 2) Normal.nucleoli=1,2,3,4,5,6,7,8 509 250 benign (0.5088409 0.4911591) *
## 3) Normal.nucleoli=9,10 37 0 malignant (0.0000000 1.0000000) *
##
## $trees[[59]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 261 malignant (0.4780220 0.5219780)
## 2) Cl.thickness=1,2,3,4,5,6,7,8 505 244 benign (0.5168317 0.4831683) *
## 3) Cl.thickness=9,10 41 0 malignant (0.0000000 1.0000000) *
##
## $trees[[60]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 238 malignant (0.4358974 0.5641026)
## 2) Normal.nucleoli=2,5,7 96 26 benign (0.7291667 0.2708333) *
## 3) Normal.nucleoli=1,3,4,6,8,9,10 450 168 malignant (0.3733333 0.6266667) *
##
## $trees[[61]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 255 malignant (0.4670330 0.5329670)
## 2) Cl.thickness=1,2,3,4,5,6,7,8 500 245 benign (0.5100000 0.4900000) *
## 3) Cl.thickness=9,10 46 0 malignant (0.0000000 1.0000000) *
##
## $trees[[62]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 252 malignant (0.4615385 0.5384615)
## 2) Epith.c.size=1,2,3,4,5,6,7 501 249 benign (0.5029940 0.4970060) *
## 3) Epith.c.size=8,9,10 45 0 malignant (0.0000000 1.0000000) *
##
## $trees[[63]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 254 malignant (0.4652015 0.5347985)
## 2) Cl.thickness=1,2,3,4,5,6,7,8 505 251 benign (0.5029703 0.4970297) *
## 3) Cl.thickness=9,10 41 0 malignant (0.0000000 1.0000000) *
##
## $trees[[64]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 228 malignant (0.4175824 0.5824176)
## 2) Cell.size=4,5,6,7,8,9,10 241 106 benign (0.5601660 0.4398340) *
## 3) Cell.size=1,2,3 305 93 malignant (0.3049180 0.6950820) *
##
## $trees[[65]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 260 malignant (0.4761905 0.5238095)
## 2) Cell.size=1,2,3,4 402 178 benign (0.5572139 0.4427861) *
## 3) Cell.size=5,6,7,8,9,10 144 36 malignant (0.2500000 0.7500000) *
##
## $trees[[66]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 230 malignant (0.42124542 0.57875458)
## 2) Bl.cromatin=1 36 2 benign (0.94444444 0.05555556) *
## 3) Bl.cromatin=2,3,4,5,6,7,8,9,10 510 196 malignant (0.38431373 0.61568627) *
##
## $trees[[67]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 259 malignant (0.4743590 0.5256410)
## 2) Normal.nucleoli=1,2,3,4,5,6,7,8 506 247 benign (0.5118577 0.4881423) *
## 3) Normal.nucleoli=9,10 40 0 malignant (0.0000000 1.0000000) *
##
## $trees[[68]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 252 malignant (0.4615385 0.5384615)
## 2) Bare.nuclei=1,2,3,7,8 317 136 benign (0.5709779 0.4290221) *
## 3) Bare.nuclei=4,5,6,9,10 229 71 malignant (0.3100437 0.6899563) *
##
## $trees[[69]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 271 benign (0.5036630 0.4963370)
## 2) Epith.c.size=1,2,3,4,5,6,7 499 224 benign (0.5511022 0.4488978) *
## 3) Epith.c.size=8,9,10 47 0 malignant (0.0000000 1.0000000) *
##
## $trees[[70]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 244 malignant (0.4468864 0.5531136)
## 2) Bare.nuclei=1,3,5,7 304 126 benign (0.5855263 0.4144737) *
## 3) Bare.nuclei=2,4,6,8,9,10 242 66 malignant (0.2727273 0.7272727) *
##
## $trees[[71]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 244 malignant (0.4468864 0.5531136)
## 2) Cl.thickness=1,2,3,4,5,6 435 212 benign (0.5126437 0.4873563) *
## 3) Cl.thickness=7,8,9,10 111 21 malignant (0.1891892 0.8108108) *
##
## $trees[[72]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 261 malignant (0.4780220 0.5219780)
## 2) Epith.c.size=1,2,3,4,5,6,7 506 245 benign (0.5158103 0.4841897) *
## 3) Epith.c.size=8,9,10 40 0 malignant (0.0000000 1.0000000) *
##
## $trees[[73]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 199 benign (0.63553114 0.36446886)
## 2) Cell.size=1,2,3 372 30 benign (0.91935484 0.08064516) *
## 3) Cell.size=4,5,6,7,8,9,10 174 5 malignant (0.02873563 0.97126437) *
##
## $trees[[74]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 240 benign (0.56043956 0.43956044)
## 2) Bl.cromatin=1,2,3 334 45 benign (0.86526946 0.13473054) *
## 3) Bl.cromatin=4,5,6,7,8,9,10 212 17 malignant (0.08018868 0.91981132) *
##
## $trees[[75]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 264 benign (0.51648352 0.48351648)
## 2) Bare.nuclei=1,2,3,8 309 50 benign (0.83818770 0.16181230) *
## 3) Bare.nuclei=4,5,6,7,9,10 237 23 malignant (0.09704641 0.90295359) *
##
## $trees[[76]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 263 benign (0.5183150 0.4816850)
## 2) Cell.shape=1,2 233 22 benign (0.9055794 0.0944206) *
## 3) Cell.shape=3,4,5,6,7,8,9,10 313 72 malignant (0.2300319 0.7699681) *
##
## $trees[[77]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 236 benign (0.5677656 0.4322344)
## 2) Bare.nuclei=1,3,4,7 319 50 benign (0.8432602 0.1567398) *
## 3) Bare.nuclei=2,5,6,8,9,10 227 41 malignant (0.1806167 0.8193833) *
##
## $trees[[78]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 242 benign (0.5567766 0.4432234)
## 2) Cl.thickness=1,2,3,4,5,6 363 92 benign (0.7465565 0.2534435) *
## 3) Cl.thickness=7,8,9,10 183 33 malignant (0.1803279 0.8196721) *
##
## $trees[[79]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 268 malignant (0.4908425 0.5091575)
## 2) Normal.nucleoli=1,2,7,8 347 107 benign (0.6916427 0.3083573) *
## 3) Normal.nucleoli=3,4,5,6,9,10 199 28 malignant (0.1407035 0.8592965) *
##
## $trees[[80]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 261 malignant (0.4780220 0.5219780)
## 2) Bare.nuclei=1,7 213 53 benign (0.7511737 0.2488263) *
## 3) Bare.nuclei=2,3,4,5,6,8,9,10 333 101 malignant (0.3033033 0.6966967) *
##
## $trees[[81]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 270 malignant (0.49450549 0.50549451)
## 2) Marg.adhesion=1,2,3,4,5 447 180 benign (0.59731544 0.40268456) *
## 3) Marg.adhesion=6,7,8,9,10 99 3 malignant (0.03030303 0.96969697) *
##
## $trees[[82]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 226 malignant (0.4139194 0.5860806)
## 2) Cl.thickness=1,2,3,4,5,6,7,8 441 215 benign (0.5124717 0.4875283) *
## 3) Cl.thickness=9,10 105 0 malignant (0.0000000 1.0000000) *
##
## $trees[[83]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 230 malignant (0.4212454 0.5787546)
## 2) Cell.size=1 112 20 benign (0.8214286 0.1785714) *
## 3) Cell.size=2,3,4,5,6,7,8,9,10 434 138 malignant (0.3179724 0.6820276) *
##
## $trees[[84]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 243 malignant (0.4450549 0.5549451)
## 2) Cl.thickness=1,2,3,4,5,6,7,8 458 215 benign (0.5305677 0.4694323) *
## 3) Cl.thickness=9,10 88 0 malignant (0.0000000 1.0000000) *
##
## $trees[[85]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 234 malignant (0.4285714 0.5714286)
## 2) Normal.nucleoli=1,2,3,7,8 384 174 benign (0.5468750 0.4531250) *
## 3) Normal.nucleoli=4,5,6,9,10 162 24 malignant (0.1481481 0.8518519) *
##
## $trees[[86]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 207 malignant (0.3791209 0.6208791)
## 2) Normal.nucleoli=2,3,4,5,7 169 65 benign (0.6153846 0.3846154) *
## 3) Normal.nucleoli=1,6,8,9,10 377 103 malignant (0.2732095 0.7267905) *
##
## $trees[[87]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 238 malignant (0.4358974 0.5641026)
## 2) Cl.thickness=1,2,3 109 18 benign (0.8348624 0.1651376) *
## 3) Cl.thickness=4,5,6,7,8,9,10 437 147 malignant (0.3363844 0.6636156) *
##
## $trees[[88]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 260 malignant (0.4761905 0.5238095)
## 2) Cl.thickness=1,2,3,4,5,6,7,8 472 212 benign (0.5508475 0.4491525) *
## 3) Cl.thickness=9,10 74 0 malignant (0.0000000 1.0000000) *
##
## $trees[[89]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 237 malignant (0.43406593 0.56593407)
## 2) Marg.adhesion=1,2,3,4,5 464 228 benign (0.50862069 0.49137931) *
## 3) Marg.adhesion=6,7,8,9,10 82 1 malignant (0.01219512 0.98780488) *
##
## $trees[[90]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 232 malignant (0.4249084 0.5750916)
## 2) Bare.nuclei=1,2,7,8 267 108 benign (0.5955056 0.4044944) *
## 3) Bare.nuclei=3,4,5,6,9,10 279 73 malignant (0.2616487 0.7383513) *
##
## $trees[[91]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 204 malignant (0.3736264 0.6263736)
## 2) Normal.nucleoli=2,7 70 15 benign (0.7857143 0.2142857) *
## 3) Normal.nucleoli=1,3,4,5,6,8,9,10 476 149 malignant (0.3130252 0.6869748) *
##
## $trees[[92]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 258 malignant (0.4725275 0.5274725)
## 2) Cl.thickness=1,2,3,4,5,6,7,8 485 227 benign (0.5319588 0.4680412) *
## 3) Cl.thickness=9,10 61 0 malignant (0.0000000 1.0000000) *
##
## $trees[[93]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 233 malignant (0.4267399 0.5732601)
## 2) Cell.size=1,2,3,4 396 185 benign (0.5328283 0.4671717) *
## 3) Cell.size=5,6,7,8,9,10 150 22 malignant (0.1466667 0.8533333) *
##
## $trees[[94]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 220 malignant (0.4029304 0.5970696)
## 2) Bare.nuclei=1,3,7 203 81 benign (0.6009852 0.3990148) *
## 3) Bare.nuclei=2,4,5,6,8,9,10 343 98 malignant (0.2857143 0.7142857) *
##
## $trees[[95]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 229 malignant (0.4194139 0.5805861)
## 2) Normal.nucleoli=2,7,8 117 28 benign (0.7606838 0.2393162) *
## 3) Normal.nucleoli=1,3,4,5,6,9,10 429 140 malignant (0.3263403 0.6736597) *
##
## $trees[[96]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 235 malignant (0.4304029 0.5695971)
## 2) Bl.cromatin=1,4,6 128 36 benign (0.7187500 0.2812500) *
## 3) Bl.cromatin=2,3,5,7,8,9,10 418 143 malignant (0.3421053 0.6578947) *
##
## $trees[[97]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 268 benign (0.50915751 0.49084249)
## 2) Marg.adhesion=1,2,3,4,5 476 201 benign (0.57773109 0.42226891) *
## 3) Marg.adhesion=6,7,8,9,10 70 3 malignant (0.04285714 0.95714286) *
##
## $trees[[98]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 265 malignant (0.4853480 0.5146520)
## 2) Cl.thickness=1,2,3,4,5,6,7,8 486 221 benign (0.5452675 0.4547325) *
## 3) Cl.thickness=9,10 60 0 malignant (0.0000000 1.0000000) *
##
## $trees[[99]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 250 malignant (0.4578755 0.5421245)
## 2) Epith.c.size=1,2,3,4,5 397 170 benign (0.5717884 0.4282116) *
## 3) Epith.c.size=6,7,8,9,10 149 23 malignant (0.1543624 0.8456376) *
##
## $trees[[100]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 229 malignant (0.4194139 0.5805861)
## 2) Cell.size=1,2,3,4 402 199 benign (0.5049751 0.4950249) *
## 3) Cell.size=5,6,7,8,9,10 144 26 malignant (0.1805556 0.8194444) *
##
##
## $weights
## [1] 1.310765052 1.098193386 0.744729814 0.760795644 0.619590175 0.667583831
## [7] 0.540224251 0.431172154 0.271223408 0.257515207 0.445677057 0.201016818
## [13] 0.301882404 0.154346242 0.100870271 0.313742349 0.355142225 0.141122321
## [19] 0.070561160 0.101132284 0.322822024 0.195828069 0.183223260 0.311625106
## [25] 0.123330651 0.298653289 0.238841556 0.089402605 0.257643363 0.095174992
## [31] 0.257030988 0.154473129 0.144841572 0.066973749 0.259388583 0.098148216
## [37] 0.263893558 0.175005835 0.166518954 0.109777570 0.086666404 0.114926417
## [43] 0.026361468 0.240411090 0.222013073 0.164014597 0.095805879 0.103327014
## [49] 0.026650339 0.247706034 0.087750351 0.213122096 0.190496529 0.081506584
## [55] 0.040753292 0.231081379 0.099590924 0.049795462 0.045614560 0.207796509
## [61] 0.085130806 0.040726716 0.027527545 0.212363859 0.140550746 0.222395714
## [67] 0.069910159 0.142640627 0.046359831 0.116365740 0.139049475 0.002000003
## [73] 1.310765052 0.944243219 0.919010212 0.776828922 0.613572402 0.580263569
## [79] 0.539795808 0.454971535 0.330696573 0.210206849 0.467013684 0.190833590
## [85] 0.215616117 0.283919536 0.362334520 0.179644467 0.177090153 0.306776649
## [91] 0.354065619 0.156472902 0.229370789 0.284700369 0.320117508 0.279358555
## [97] 0.226118172 0.129079772 0.121452046 0.106233711
##
## $votes
## [,1] [,2]
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## [452,] 4.9973657 22.593481
## [453,] 3.3395133 24.251333
## [454,] 4.2040521 23.386795
## [455,] 7.8373797 19.753467
## [456,] 11.3811957 16.209651
## [457,] 4.5926642 22.998182
## [458,] 4.4895188 23.101328
## [459,] 3.7571901 23.833657
## [460,] 9.3268487 18.263998
## [461,] 8.7702493 18.820597
## [462,] 5.1043028 22.486544
## [463,] 12.1561180 15.434729
## [464,] 6.9481409 20.642706
## [465,] 12.5437922 15.047054
## [466,] 7.8169942 19.773852
## [467,] 4.2421667 23.348680
## [468,] 10.7780037 16.812843
## [469,] 4.1149656 23.475881
## [470,] 3.1437070 24.447140
## [471,] 7.6971348 19.893712
## [472,] 3.8586739 23.732173
## [473,] 7.2637311 20.327116
## [474,] 10.0207142 17.570132
## [475,] 4.9844937 22.606353
## [476,] 14.2966039 13.294243
## [477,] 5.8255614 21.765285
## [478,] 12.5169526 15.073894
## [479,] 7.6774463 19.913400
## [480,] 14.5785331 13.012314
## [481,] 11.8734179 15.717429
## [482,] 2.8896336 24.701213
## [483,] 2.8944837 24.696363
## [484,] 13.3065267 14.284320
## [485,] 9.1413960 18.449451
## [486,] 6.6409905 20.949856
## [487,] 5.8995467 21.691300
## [488,] 2.6090170 24.981830
## [489,] 1.8357055 25.755141
## [490,] 4.9567341 22.634113
## [491,] 10.4650508 17.125796
## [492,] 6.5279594 21.062887
## [493,] 10.4830887 17.107758
## [494,] 8.6924841 18.898363
## [495,] 6.9403511 20.650496
## [496,] 14.3010627 13.289784
## [497,] 6.7075990 20.883248
## [498,] 5.5375482 22.053298
## [499,] 7.9141348 19.676712
## [500,] 4.4895188 23.101328
## [501,] 8.8125080 18.778339
## [502,] 4.2174597 23.373387
## [503,] 1.2017010 26.389146
## [504,] 5.4611228 22.129724
## [505,] 9.4637199 18.127127
## [506,] 8.8593358 18.731511
## [507,] 14.8031562 12.787690
## [508,] 0.3014504 27.289396
## [509,] 10.2394156 17.351431
## [510,] 6.1960175 21.394829
## [511,] 9.7140985 17.876748
## [512,] 9.2894748 18.301372
## [513,] 6.2886228 21.302224
## [514,] 7.9482964 19.642550
## [515,] 2.6090170 24.981830
## [516,] 3.5058243 24.085022
## [517,] 6.1876993 21.403147
## [518,] 9.4877260 18.103121
## [519,] 10.9728344 16.618012
## [520,] 3.5579417 24.032905
## [521,] 5.4820155 22.108831
## [522,] 0.2123639 27.378483
## [523,] 10.0571926 17.533654
## [524,] 4.9388632 22.651983
## [525,] 7.4605434 20.130303
## [526,] 2.1400523 25.450794
## [527,] 5.9131754 21.677671
## [528,] 7.3135452 20.277301
## [529,] 15.2676521 12.323195
## [530,] 4.4895188 23.101328
## [531,] 5.2911975 22.299649
## [532,] 5.1813089 22.409538
## [533,] 13.7422621 13.848585
## [534,] 4.3536488 23.237198
## [535,] 7.9213404 19.669506
## [536,] 8.7270348 18.863812
## [537,] 6.1876993 21.403147
## [538,] 12.6520791 14.938768
## [539,] 8.6546794 18.936167
## [540,] 4.5178253 23.073021
## [541,] 12.5421487 15.048698
## [542,] 9.2831335 18.307713
## [543,] 2.9686713 24.622175
## [544,] 11.4560891 16.134758
## [545,] 8.4946146 19.096232
## [546,] 3.7882541 23.802593
##
## $prob
## [,1] [,2]
## [1,] 0.812858603 0.18714140
## [2,] 0.910426638 0.08957336
## [3,] 0.876610593 0.12338941
## [4,] 0.876610593 0.12338941
## [5,] 0.825806367 0.17419363
## [6,] 0.761658061 0.23834194
## [7,] 0.830953731 0.16904627
## [8,] 0.863738852 0.13626115
## [9,] 0.804949573 0.19505043
## [10,] 0.812673950 0.18732605
## [11,] 0.910426638 0.08957336
## [12,] 0.876610593 0.12338941
## [13,] 0.850606434 0.14939357
## [14,] 0.717875386 0.28212461
## [15,] 0.810392421 0.18960758
## [16,] 0.876610593 0.12338941
## [17,] 0.525587937 0.47441206
## [18,] 0.665587275 0.33441273
## [19,] 0.868886216 0.13111378
## [20,] 0.812673950 0.18732605
## [21,] 0.856014475 0.14398553
## [22,] 0.825806367 0.17419363
## [23,] 0.806688119 0.19331188
## [24,] 0.876610593 0.12338941
## [25,] 0.746113334 0.25388667
## [26,] 0.787797859 0.21220214
## [27,] 0.838678108 0.16132189
## [28,] 0.805576376 0.19442362
## [29,] 0.838678108 0.16132189
## [30,] 0.818081990 0.18191801
## [31,] 0.812673950 0.18732605
## [32,] 0.812673950 0.18732605
## [33,] 0.825806367 0.17419363
## [34,] 0.787035416 0.21296458
## [35,] 0.850606434 0.14939357
## [36,] 0.807258278 0.19274172
## [37,] 0.876610593 0.12338941
## [38,] 0.850606434 0.14939357
## [39,] 0.784388263 0.21561174
## [40,] 0.825806367 0.17419363
## [41,] 0.812673950 0.18732605
## [42,] 0.910426638 0.08957336
## [43,] 0.812673950 0.18732605
## [44,] 0.755807870 0.24419213
## [45,] 0.842882057 0.15711794
## [46,] 0.876610593 0.12338941
## [47,] 0.910426638 0.08957336
## [48,] 0.936430796 0.06356920
## [49,] 0.848916901 0.15108310
## [50,] 0.838678108 0.16132189
## [51,] 0.812673950 0.18732605
## [52,] 0.505650347 0.49434965
## [53,] 0.812673950 0.18732605
## [54,] 0.812673950 0.18732605
## [55,] 0.825806367 0.17419363
## [56,] 0.838678108 0.16132189
## [57,] 0.838678108 0.16132189
## [58,] 0.843508860 0.15649114
## [59,] 0.850606434 0.14939357
## [60,] 0.447010635 0.55298937
## [61,] 0.612729228 0.38727077
## [62,] 0.838678108 0.16132189
## [63,] 0.876610593 0.12338941
## [64,] 0.807258278 0.19274172
## [65,] 0.948359122 0.05164088
## [66,] 0.838678108 0.16132189
## [67,] 0.876610593 0.12338941
## [68,] 0.761793701 0.23820630
## [69,] 0.804998586 0.19500141
## [70,] 0.722488075 0.27751193
## [71,] 0.802668044 0.19733196
## [72,] 0.838678108 0.16132189
## [73,] 0.812673950 0.18732605
## [74,] 0.838678108 0.16132189
## [75,] 0.492715575 0.50728442
## [76,] 0.717875386 0.28212461
## [77,] 0.812673950 0.18732605
## [78,] 0.825806367 0.17419363
## [79,] 0.850606434 0.14939357
## [80,] 0.910426638 0.08957336
## [81,] 0.766457837 0.23354216
## [82,] 0.812673950 0.18732605
## [83,] 0.759588196 0.24041180
## [84,] 0.825806367 0.17419363
## [85,] 0.804949573 0.19505043
## [86,] 0.606982649 0.39301735
## [87,] 0.804949573 0.19505043
## [88,] 0.812673950 0.18732605
## [89,] 0.850606434 0.14939357
## [90,] 0.838678108 0.16132189
## [91,] 0.876610593 0.12338941
## [92,] 0.790695599 0.20930440
## [93,] 0.876610593 0.12338941
## [94,] 0.663304036 0.33669596
## [95,] 0.842882057 0.15711794
## [96,] 0.543776773 0.45622323
## [97,] 0.876610593 0.12338941
## [98,] 0.850606434 0.14939357
## [99,] 0.838678108 0.16132189
## [100,] 0.693971877 0.30602812
## [101,] 0.838678108 0.16132189
## [102,] 0.838678108 0.16132189
## [103,] 0.805576376 0.19442362
## [104,] 0.743879544 0.25612046
## [105,] 0.799590545 0.20040945
## [106,] 0.812673950 0.18732605
## [107,] 0.825806367 0.17419363
## [108,] 0.791866168 0.20813383
## [109,] 0.842882057 0.15711794
## [110,] 0.525709203 0.47429080
## [111,] 0.850606434 0.14939357
## [112,] 0.825806367 0.17419363
## [113,] 0.922720589 0.07727941
## [114,] 0.876610593 0.12338941
## [115,] 0.842882057 0.15711794
## [116,] 0.876610593 0.12338941
## [117,] 0.657463863 0.34253614
## [118,] 0.825806367 0.17419363
## [119,] 0.804949573 0.19505043
## [120,] 0.876610593 0.12338941
## [121,] 0.507872730 0.49212727
## [122,] 0.876610593 0.12338941
## [123,] 0.805576376 0.19442362
## [124,] 0.842882057 0.15711794
## [125,] 0.838678108 0.16132189
## [126,] 0.818081990 0.18191801
## [127,] 0.876610593 0.12338941
## [128,] 0.805576376 0.19442362
## [129,] 0.876610593 0.12338941
## [130,] 0.825806367 0.17419363
## [131,] 0.780073482 0.21992652
## [132,] 0.838678108 0.16132189
## [133,] 0.812673950 0.18732605
## [134,] 0.850606434 0.14939357
## [135,] 0.856617932 0.14338207
## [136,] 0.748632050 0.25136795
## [137,] 0.761793701 0.23820630
## [138,] 0.818708793 0.18129121
## [139,] 0.863738852 0.13626115
## [140,] 0.825806367 0.17419363
## [141,] 0.735509748 0.26449025
## [142,] 0.863738852 0.13626115
## [143,] 0.825806367 0.17419363
## [144,] 0.876610593 0.12338941
## [145,] 0.843508860 0.15649114
## [146,] 0.825594704 0.17440530
## [147,] 0.876610593 0.12338941
## [148,] 0.902702261 0.09729774
## [149,] 0.838678108 0.16132189
## [150,] 0.876610593 0.12338941
## [151,] 0.825806367 0.17419363
## [152,] 0.863738852 0.13626115
## [153,] 0.825806367 0.17419363
## [154,] 0.863738852 0.13626115
## [155,] 0.812673950 0.18732605
## [156,] 0.825806367 0.17419363
## [157,] 0.784045819 0.21595418
## [158,] 0.876610593 0.12338941
## [159,] 0.621154002 0.37884600
## [160,] 0.838678108 0.16132189
## [161,] 0.755807870 0.24419213
## [162,] 0.902751274 0.09724873
## [163,] 0.787797859 0.21220214
## [164,] 0.838678108 0.16132189
## [165,] 0.838678108 0.16132189
## [166,] 0.812673950 0.18732605
## [167,] 0.819820537 0.18017946
## [168,] 0.746455778 0.25354422
## [169,] 0.830953731 0.16904627
## [170,] 0.838678108 0.16132189
## [171,] 0.761793701 0.23820630
## [172,] 0.876610593 0.12338941
## [173,] 0.812673950 0.18732605
## [174,] 0.876610593 0.12338941
## [175,] 0.690909372 0.30909063
## [176,] 0.748877820 0.25112218
## [177,] 0.825806367 0.17419363
## [178,] 0.838678108 0.16132189
## [179,] 0.838678108 0.16132189
## [180,] 0.829349526 0.17065047
## [181,] 0.850606434 0.14939357
## [182,] 0.772459936 0.22754006
## [183,] 0.457014128 0.54298587
## [184,] 0.812673950 0.18732605
## [185,] 0.876610593 0.12338941
## [186,] 0.863738852 0.13626115
## [187,] 0.850606434 0.14939357
## [188,] 0.863738852 0.13626115
## [189,] 0.923559055 0.07644094
## [190,] 0.838678108 0.16132189
## [191,] 0.838678108 0.16132189
## [192,] 0.868886216 0.13111378
## [193,] 0.838678108 0.16132189
## [194,] 0.812673950 0.18732605
## [195,] 0.825806367 0.17419363
## [196,] 0.876610593 0.12338941
## [197,] 0.825806367 0.17419363
## [198,] 0.812673950 0.18732605
## [199,] 0.781812028 0.21818797
## [200,] 0.575005128 0.42499487
## [201,] 0.838678108 0.16132189
## [202,] 0.868886216 0.13111378
## [203,] 0.761793701 0.23820630
## [204,] 0.709505590 0.29049441
## [205,] 0.838678108 0.16132189
## [206,] 0.876610593 0.12338941
## [207,] 0.478973738 0.52102626
## [208,] 0.863738852 0.13626115
## [209,] 0.838678108 0.16132189
## [210,] 0.761658061 0.23834194
## [211,] 0.825806367 0.17419363
## [212,] 0.459890076 0.54010992
## [213,] 0.812673950 0.18732605
## [214,] 0.812673950 0.18732605
## [215,] 0.838678108 0.16132189
## [216,] 0.812673950 0.18732605
## [217,] 0.708572307 0.29142769
## [218,] 0.838678108 0.16132189
## [219,] 0.812673950 0.18732605
## [220,] 0.818708793 0.18129121
## [221,] 0.825806367 0.17419363
## [222,] 0.806688119 0.19331188
## [223,] 0.812673950 0.18732605
## [224,] 0.799726185 0.20027381
## [225,] 0.863738852 0.13626115
## [226,] 0.850606434 0.14939357
## [227,] 0.654649415 0.34535059
## [228,] 0.876610593 0.12338941
## [229,] 0.910426638 0.08957336
## [230,] 0.863738852 0.13626115
## [231,] 0.825806367 0.17419363
## [232,] 0.876610593 0.12338941
## [233,] 0.838678108 0.16132189
## [234,] 0.876610593 0.12338941
## [235,] 0.876610593 0.12338941
## [236,] 0.735509748 0.26449025
## [237,] 0.761793701 0.23820630
## [238,] 0.852707084 0.14729292
## [239,] 0.825730344 0.17426966
## [240,] 0.856014475 0.14398553
## [241,] 0.910426638 0.08957336
## [242,] 0.838678108 0.16132189
## [243,] 0.823856157 0.17614384
## [244,] 0.838678108 0.16132189
## [245,] 0.838678108 0.16132189
## [246,] 0.843746191 0.15625381
## [247,] 0.876610593 0.12338941
## [248,] 0.876610593 0.12338941
## [249,] 0.838678108 0.16132189
## [250,] 0.768755634 0.23124437
## [251,] 0.768733711 0.23126629
## [252,] 0.814774599 0.18522540
## [253,] 0.676769483 0.32323052
## [254,] 0.650432295 0.34956771
## [255,] 0.876610593 0.12338941
## [256,] 0.825806367 0.17419363
## [257,] 0.838678108 0.16132189
## [258,] 0.850606434 0.14939357
## [259,] 0.838678108 0.16132189
## [260,] 0.825806367 0.17419363
## [261,] 0.812673950 0.18732605
## [262,] 0.863738852 0.13626115
## [263,] 0.812673950 0.18732605
## [264,] 0.787797859 0.21220214
## [265,] 0.805576376 0.19442362
## [266,] 0.859546389 0.14045361
## [267,] 0.838678108 0.16132189
## [268,] 0.842882057 0.15711794
## [269,] 0.792001809 0.20799819
## [270,] 0.801902858 0.19809714
## [271,] 0.838678108 0.16132189
## [272,] 0.825806367 0.17419363
## [273,] 0.794759793 0.20524021
## [274,] 0.832692277 0.16730772
## [275,] 0.838678108 0.16132189
## [276,] 0.825806367 0.17419363
## [277,] 0.838678108 0.16132189
## [278,] 0.787797859 0.21220214
## [279,] 0.936430796 0.06356920
## [280,] 0.830953731 0.16904627
## [281,] 0.830953731 0.16904627
## [282,] 0.663304036 0.33669596
## [283,] 0.863738852 0.13626115
## [284,] 0.812673950 0.18732605
## [285,] 0.812673950 0.18732605
## [286,] 0.547325666 0.45267433
## [287,] 0.812673950 0.18732605
## [288,] 0.876610593 0.12338941
## [289,] 0.838678108 0.16132189
## [290,] 0.838678108 0.16132189
## [291,] 0.850606434 0.14939357
## [292,] 0.838678108 0.16132189
## [293,] 0.825806367 0.17419363
## [294,] 0.838678108 0.16132189
## [295,] 0.703781113 0.29621889
## [296,] 0.842882057 0.15711794
## [297,] 0.710748579 0.28925142
## [298,] 0.850606434 0.14939357
## [299,] 0.556615247 0.44338475
## [300,] 0.868886216 0.13111378
## [301,] 0.761031258 0.23896874
## [302,] 0.838678108 0.16132189
## [303,] 0.838678108 0.16132189
## [304,] 0.825730344 0.17426966
## [305,] 0.802668044 0.19733196
## [306,] 0.812673950 0.18732605
## [307,] 0.761658061 0.23834194
## [308,] 0.876610593 0.12338941
## [309,] 0.825806367 0.17419363
## [310,] 0.802668044 0.19733196
## [311,] 0.804949573 0.19505043
## [312,] 0.619036689 0.38096331
## [313,] 0.843508860 0.15649114
## [314,] 0.640029123 0.35997088
## [315,] 0.876610593 0.12338941
## [316,] 0.825730344 0.17426966
## [317,] 0.767911578 0.23208842
## [318,] 0.818708793 0.18129121
## [319,] 0.843508860 0.15649114
## [320,] 0.792001809 0.20799819
## [321,] 0.812673950 0.18732605
## [322,] 0.876610593 0.12338941
## [323,] 0.838678108 0.16132189
## [324,] 0.838678108 0.16132189
## [325,] 0.809805993 0.19019401
## [326,] 0.812673950 0.18732605
## [327,] 0.838678108 0.16132189
## [328,] 0.812673950 0.18732605
## [329,] 0.812673950 0.18732605
## [330,] 0.850606434 0.14939357
## [331,] 0.722638008 0.27736199
## [332,] 0.825806367 0.17419363
## [333,] 0.467851524 0.53214848
## [334,] 0.856014475 0.14398553
## [335,] 0.825806367 0.17419363
## [336,] 0.850606434 0.14939357
## [337,] 0.838678108 0.16132189
## [338,] 0.851686372 0.14831363
## [339,] 0.850606434 0.14939357
## [340,] 0.804949573 0.19505043
## [341,] 0.805576376 0.19442362
## [342,] 0.863738852 0.13626115
## [343,] 0.818708793 0.18129121
## [344,] 0.838678108 0.16132189
## [345,] 0.626997456 0.37300254
## [346,] 0.812673950 0.18732605
## [347,] 0.831580534 0.16841947
## [348,] 0.767962660 0.23203734
## [349,] 0.825806367 0.17419363
## [350,] 0.683184995 0.31681500
## [351,] 0.850606434 0.14939357
## [352,] 0.830613774 0.16938623
## [353,] 0.923559055 0.07644094
## [354,] 0.838678108 0.16132189
## [355,] 0.710151009 0.28984899
## [356,] 0.176451396 0.82354860
## [357,] 0.281163789 0.71883621
## [358,] 0.115949898 0.88405010
## [359,] 0.452013039 0.54798696
## [360,] 0.370353816 0.62964618
## [361,] 0.335873544 0.66412646
## [362,] 0.309570021 0.69042998
## [363,] 0.270399220 0.72960078
## [364,] 0.243261189 0.75673881
## [365,] 0.224266381 0.77573362
## [366,] 0.265056402 0.73494360
## [367,] 0.379933157 0.62006684
## [368,] 0.101178110 0.89882189
## [369,] 0.556755436 0.44324456
## [370,] 0.315712117 0.68428788
## [371,] 0.156109656 0.84389034
## [372,] 0.184273337 0.81572666
## [373,] 0.181683451 0.81831655
## [374,] 0.263286619 0.73671338
## [375,] 0.292104588 0.70789541
## [376,] 0.307877998 0.69212200
## [377,] 0.283264322 0.71673568
## [378,] 0.037401776 0.96259822
## [379,] 0.340124075 0.65987593
## [380,] 0.290102540 0.70989746
## [381,] 0.171782269 0.82821773
## [382,] 0.182510514 0.81748949
## [383,] 0.174156852 0.82584315
## [384,] 0.104907389 0.89509261
## [385,] 0.011914444 0.98808556
## [386,] 0.229197420 0.77080258
## [387,] 0.462878411 0.53712159
## [388,] 0.334245117 0.66575488
## [389,] 0.080108404 0.91989160
## [390,] 0.236642148 0.76335785
## [391,] 0.228449845 0.77155015
## [392,] 0.364982675 0.63501733
## [393,] 0.219810462 0.78018954
## [394,] 0.396988928 0.60301107
## [395,] 0.322138639 0.67786136
## [396,] 0.171424590 0.82857541
## [397,] 0.436455984 0.56354402
## [398,] 0.334245117 0.66575488
## [399,] 0.307877998 0.69212200
## [400,] 0.305058615 0.69494138
## [401,] 0.245866897 0.75413310
## [402,] 0.260057775 0.73994223
## [403,] 0.591643016 0.40835698
## [404,] 0.463332098 0.53666790
## [405,] 0.162717688 0.83728231
## [406,] 0.363885415 0.63611459
## [407,] 0.277620762 0.72237924
## [408,] 0.330570641 0.66942936
## [409,] 0.346453402 0.65354660
## [410,] 0.309010243 0.69098976
## [411,] 0.133056500 0.86694350
## [412,] 0.217257030 0.78274297
## [413,] 0.229689714 0.77031029
## [414,] 0.080108404 0.91989160
## [415,] 0.254750549 0.74524945
## [416,] 0.317782173 0.68221783
## [417,] 0.182076143 0.81792386
## [418,] 0.293528686 0.70647131
## [419,] 0.252970058 0.74702994
## [420,] 0.149142418 0.85085758
## [421,] 0.242623632 0.75737637
## [422,] 0.328408292 0.67159171
## [423,] 0.167327951 0.83267205
## [424,] 0.039211989 0.96078801
## [425,] 0.248834060 0.75116594
## [426,] 0.250067699 0.74993230
## [427,] 0.152880811 0.84711919
## [428,] 0.425502762 0.57449724
## [429,] 0.381778311 0.61822169
## [430,] 0.043554338 0.95644566
## [431,] 0.228449845 0.77155015
## [432,] 0.288530725 0.71146927
## [433,] 0.211004019 0.78899598
## [434,] 0.280788222 0.71921178
## [435,] 0.315241843 0.68475816
## [436,] 0.162274836 0.83772516
## [437,] 0.166935237 0.83306476
## [438,] 0.226049444 0.77395056
## [439,] 0.241967835 0.75803216
## [440,] 0.311003810 0.68899619
## [441,] 0.435579061 0.56442094
## [442,] 0.069761979 0.93023802
## [443,] 0.208080301 0.79191970
## [444,] 0.309163413 0.69083659
## [445,] 0.203267489 0.79673251
## [446,] 0.285263848 0.71473615
## [447,] 0.243386466 0.75661353
## [448,] 0.025489714 0.97451029
## [449,] 0.250067699 0.74993230
## [450,] 0.475087679 0.52491232
## [451,] 0.097249811 0.90275019
## [452,] 0.181124043 0.81887596
## [453,] 0.121036999 0.87896300
## [454,] 0.152371262 0.84762874
## [455,] 0.284057240 0.71594276
## [456,] 0.412498964 0.58750104
## [457,] 0.166456081 0.83354392
## [458,] 0.162717688 0.83728231
## [459,] 0.136175239 0.86382476
## [460,] 0.338041410 0.66195859
## [461,] 0.317868074 0.68213193
## [462,] 0.184999860 0.81500014
## [463,] 0.440585176 0.55941482
## [464,] 0.251827751 0.74817225
## [465,] 0.454636001 0.54536400
## [466,] 0.283318388 0.71668161
## [467,] 0.153752681 0.84624732
## [468,] 0.390636931 0.60936307
## [469,] 0.149142418 0.85085758
## [470,] 0.113940214 0.88605979
## [471,] 0.278974216 0.72102578
## [472,] 0.139853407 0.86014659
## [473,] 0.263265972 0.73673403
## [474,] 0.363189806 0.63681019
## [475,] 0.180657512 0.81934249
## [476,] 0.518164739 0.48183526
## [477,] 0.211141088 0.78885891
## [478,] 0.453663230 0.54633677
## [479,] 0.278260626 0.72173937
## [480,] 0.528382955 0.47161705
## [481,] 0.430339019 0.56966098
## [482,] 0.104731603 0.89526840
## [483,] 0.104907389 0.89509261
## [484,] 0.482280477 0.51771952
## [485,] 0.331319881 0.66868012
## [486,] 0.240695424 0.75930458
## [487,] 0.213822605 0.78617739
## [488,] 0.094560963 0.90543904
## [489,] 0.066533134 0.93346687
## [490,] 0.179651396 0.82034860
## [491,] 0.379294299 0.62070570
## [492,] 0.236598734 0.76340127
## [493,] 0.379948061 0.62005194
## [494,] 0.315049561 0.68495044
## [495,] 0.251545418 0.74845458
## [496,] 0.518326345 0.48167366
## [497,] 0.243109576 0.75689042
## [498,] 0.200702364 0.79929764
## [499,] 0.286839143 0.71316086
## [500,] 0.162717688 0.83728231
## [501,] 0.319399695 0.68060030
## [502,] 0.152857206 0.84714279
## [503,] 0.043554338 0.95644566
## [504,] 0.197932411 0.80206759
## [505,] 0.343002156 0.65699784
## [506,] 0.321096918 0.67890308
## [507,] 0.536524172 0.46347583
## [508,] 0.010925740 0.98907426
## [509,] 0.371116396 0.62888360
## [510,] 0.224567864 0.77543214
## [511,] 0.352076856 0.64792314
## [512,] 0.336686832 0.66331317
## [513,] 0.227924243 0.77207576
## [514,] 0.288077292 0.71192271
## [515,] 0.094560963 0.90543904
## [516,] 0.127064761 0.87293524
## [517,] 0.224266381 0.77573362
## [518,] 0.343872232 0.65612777
## [519,] 0.397698357 0.60230164
## [520,] 0.128953697 0.87104630
## [521,] 0.198689646 0.80131035
## [522,] 0.007696895 0.99230310
## [523,] 0.364511922 0.63548808
## [524,] 0.179003686 0.82099631
## [525,] 0.270399220 0.72960078
## [526,] 0.077563851 0.92243615
## [527,] 0.214316562 0.78568344
## [528,] 0.265071431 0.73492857
## [529,] 0.553359318 0.44664068
## [530,] 0.162717688 0.83728231
## [531,] 0.191773655 0.80822635
## [532,] 0.187790864 0.81220914
## [533,] 0.498073231 0.50192677
## [534,] 0.157793231 0.84220677
## [535,] 0.287100302 0.71289970
## [536,] 0.316301814 0.68369819
## [537,] 0.224266381 0.77573362
## [538,] 0.458560741 0.54143926
## [539,] 0.313679370 0.68632063
## [540,] 0.163743627 0.83625637
## [541,] 0.454576434 0.54542357
## [542,] 0.336457000 0.66354300
## [543,] 0.107596237 0.89240376
## [544,] 0.415213396 0.58478660
## [545,] 0.307877998 0.69212200
## [546,] 0.137301117 0.86269888
##
## $class
## [1] "benign" "benign" "benign" "benign" "benign" "benign"
## [7] "benign" "benign" "benign" "benign" "benign" "benign"
## [13] "benign" "benign" "benign" "benign" "benign" "benign"
## [19] "benign" "benign" "benign" "benign" "benign" "benign"
## [25] "benign" "benign" "benign" "benign" "benign" "benign"
## [31] "benign" "benign" "benign" "benign" "benign" "benign"
## [37] "benign" "benign" "benign" "benign" "benign" "benign"
## [43] "benign" "benign" "benign" "benign" "benign" "benign"
## [49] "benign" "benign" "benign" "benign" "benign" "benign"
## [55] "benign" "benign" "benign" "benign" "benign" "malignant"
## [61] "benign" "benign" "benign" "benign" "benign" "benign"
## [67] "benign" "benign" "benign" "benign" "benign" "benign"
## [73] "benign" "benign" "malignant" "benign" "benign" "benign"
## [79] "benign" "benign" "benign" "benign" "benign" "benign"
## [85] "benign" "benign" "benign" "benign" "benign" "benign"
## [91] "benign" "benign" "benign" "benign" "benign" "benign"
## [97] "benign" "benign" "benign" "benign" "benign" "benign"
## [103] "benign" "benign" "benign" "benign" "benign" "benign"
## [109] "benign" "benign" "benign" "benign" "benign" "benign"
## [115] "benign" "benign" "benign" "benign" "benign" "benign"
## [121] "benign" "benign" "benign" "benign" "benign" "benign"
## [127] "benign" "benign" "benign" "benign" "benign" "benign"
## [133] "benign" "benign" "benign" "benign" "benign" "benign"
## [139] "benign" "benign" "benign" "benign" "benign" "benign"
## [145] "benign" "benign" "benign" "benign" "benign" "benign"
## [151] "benign" "benign" "benign" "benign" "benign" "benign"
## [157] "benign" "benign" "benign" "benign" "benign" "benign"
## [163] "benign" "benign" "benign" "benign" "benign" "benign"
## [169] "benign" "benign" "benign" "benign" "benign" "benign"
## [175] "benign" "benign" "benign" "benign" "benign" "benign"
## [181] "benign" "benign" "malignant" "benign" "benign" "benign"
## [187] "benign" "benign" "benign" "benign" "benign" "benign"
## [193] "benign" "benign" "benign" "benign" "benign" "benign"
## [199] "benign" "benign" "benign" "benign" "benign" "benign"
## [205] "benign" "benign" "malignant" "benign" "benign" "benign"
## [211] "benign" "malignant" "benign" "benign" "benign" "benign"
## [217] "benign" "benign" "benign" "benign" "benign" "benign"
## [223] "benign" "benign" "benign" "benign" "benign" "benign"
## [229] "benign" "benign" "benign" "benign" "benign" "benign"
## [235] "benign" "benign" "benign" "benign" "benign" "benign"
## [241] "benign" "benign" "benign" "benign" "benign" "benign"
## [247] "benign" "benign" "benign" "benign" "benign" "benign"
## [253] "benign" "benign" "benign" "benign" "benign" "benign"
## [259] "benign" "benign" "benign" "benign" "benign" "benign"
## [265] "benign" "benign" "benign" "benign" "benign" "benign"
## [271] "benign" "benign" "benign" "benign" "benign" "benign"
## [277] "benign" "benign" "benign" "benign" "benign" "benign"
## [283] "benign" "benign" "benign" "benign" "benign" "benign"
## [289] "benign" "benign" "benign" "benign" "benign" "benign"
## [295] "benign" "benign" "benign" "benign" "benign" "benign"
## [301] "benign" "benign" "benign" "benign" "benign" "benign"
## [307] "benign" "benign" "benign" "benign" "benign" "benign"
## [313] "benign" "benign" "benign" "benign" "benign" "benign"
## [319] "benign" "benign" "benign" "benign" "benign" "benign"
## [325] "benign" "benign" "benign" "benign" "benign" "benign"
## [331] "benign" "benign" "malignant" "benign" "benign" "benign"
## [337] "benign" "benign" "benign" "benign" "benign" "benign"
## [343] "benign" "benign" "benign" "benign" "benign" "benign"
## [349] "benign" "benign" "benign" "benign" "benign" "benign"
## [355] "benign" "malignant" "malignant" "malignant" "malignant" "malignant"
## [361] "malignant" "malignant" "malignant" "malignant" "malignant" "malignant"
## [367] "malignant" "malignant" "benign" "malignant" "malignant" "malignant"
## [373] "malignant" "malignant" "malignant" "malignant" "malignant" "malignant"
## [379] "malignant" "malignant" "malignant" "malignant" "malignant" "malignant"
## [385] "malignant" "malignant" "malignant" "malignant" "malignant" "malignant"
## [391] "malignant" "malignant" "malignant" "malignant" "malignant" "malignant"
## [397] "malignant" "malignant" "malignant" "malignant" "malignant" "malignant"
## [403] "benign" "malignant" "malignant" "malignant" "malignant" "malignant"
## [409] "malignant" "malignant" "malignant" "malignant" "malignant" "malignant"
## [415] "malignant" "malignant" "malignant" "malignant" "malignant" "malignant"
## [421] "malignant" "malignant" "malignant" "malignant" "malignant" "malignant"
## [427] "malignant" "malignant" "malignant" "malignant" "malignant" "malignant"
## [433] "malignant" "malignant" "malignant" "malignant" "malignant" "malignant"
## [439] "malignant" "malignant" "malignant" "malignant" "malignant" "malignant"
## [445] "malignant" "malignant" "malignant" "malignant" "malignant" "malignant"
## [451] "malignant" "malignant" "malignant" "malignant" "malignant" "malignant"
## [457] "malignant" "malignant" "malignant" "malignant" "malignant" "malignant"
## [463] "malignant" "malignant" "malignant" "malignant" "malignant" "malignant"
## [469] "malignant" "malignant" "malignant" "malignant" "malignant" "malignant"
## [475] "malignant" "benign" "malignant" "malignant" "malignant" "benign"
## [481] "malignant" "malignant" "malignant" "malignant" "malignant" "malignant"
## [487] "malignant" "malignant" "malignant" "malignant" "malignant" "malignant"
## [493] "malignant" "malignant" "malignant" "benign" "malignant" "malignant"
## [499] "malignant" "malignant" "malignant" "malignant" "malignant" "malignant"
## [505] "malignant" "malignant" "benign" "malignant" "malignant" "malignant"
## [511] "malignant" "malignant" "malignant" "malignant" "malignant" "malignant"
## [517] "malignant" "malignant" "malignant" "malignant" "malignant" "malignant"
## [523] "malignant" "malignant" "malignant" "malignant" "malignant" "malignant"
## [529] "benign" "malignant" "malignant" "malignant" "malignant" "malignant"
## [535] "malignant" "malignant" "malignant" "malignant" "malignant" "malignant"
## [541] "malignant" "malignant" "malignant" "malignant" "malignant" "malignant"
##
## $importance
## Bare.nuclei Bl.cromatin Cell.shape Cell.size Cl.thickness
## 26.7102454 11.3512607 5.9163392 30.3905025 11.5209877
## Epith.c.size Marg.adhesion Mitoses Normal.nucleoli
## 0.5171436 3.2975425 0.5911457 9.7048326
##
## $terms
## Class ~ Cl.thickness + Cell.size + Cell.shape + Marg.adhesion +
## Epith.c.size + Bare.nuclei + Bl.cromatin + Normal.nucleoli +
## Mitoses
## attr(,"variables")
## list(Class, Cl.thickness, Cell.size, Cell.shape, Marg.adhesion,
## Epith.c.size, Bare.nuclei, Bl.cromatin, Normal.nucleoli,
## Mitoses)
## attr(,"factors")
## Cl.thickness Cell.size Cell.shape Marg.adhesion Epith.c.size
## Class 0 0 0 0 0
## Cl.thickness 1 0 0 0 0
## Cell.size 0 1 0 0 0
## Cell.shape 0 0 1 0 0
## Marg.adhesion 0 0 0 1 0
## Epith.c.size 0 0 0 0 1
## Bare.nuclei 0 0 0 0 0
## Bl.cromatin 0 0 0 0 0
## Normal.nucleoli 0 0 0 0 0
## Mitoses 0 0 0 0 0
## Bare.nuclei Bl.cromatin Normal.nucleoli Mitoses
## Class 0 0 0 0
## Cl.thickness 0 0 0 0
## Cell.size 0 0 0 0
## Cell.shape 0 0 0 0
## Marg.adhesion 0 0 0 0
## Epith.c.size 0 0 0 0
## Bare.nuclei 1 0 0 0
## Bl.cromatin 0 1 0 0
## Normal.nucleoli 0 0 1 0
## Mitoses 0 0 0 1
## attr(,"term.labels")
## [1] "Cl.thickness" "Cell.size" "Cell.shape" "Marg.adhesion"
## [5] "Epith.c.size" "Bare.nuclei" "Bl.cromatin" "Normal.nucleoli"
## [9] "Mitoses"
## attr(,"order")
## [1] 1 1 1 1 1 1 1 1 1
## attr(,"intercept")
## [1] 1
## attr(,"response")
## [1] 1
## attr(,".Environment")
## <environment: R_GlobalEnv>
## attr(,"predvars")
## list(Class, Cl.thickness, Cell.size, Cell.shape, Marg.adhesion,
## Epith.c.size, Bare.nuclei, Bl.cromatin, Normal.nucleoli,
## Mitoses)
## attr(,"dataClasses")
## Class Cl.thickness Cell.size Cell.shape Marg.adhesion
## "factor" "ordered" "ordered" "ordered" "ordered"
## Epith.c.size Bare.nuclei Bl.cromatin Normal.nucleoli Mitoses
## "ordered" "factor" "factor" "factor" "factor"
##
## $call
## adabag::boosting(formula = Class ~ ., data = data_train, mfinal = 100,
## control = rpart::rpart.control(maxdepth = 1, minsplit = 2,
## cp = 0))
##
## attr(,"vardep.summary")
## benign malignant
## 355 191
## attr(,"class")
## [1] "boosting"Model ketiga menggunakan 200 learner. Hasil model ini dibandingkan dengan model yang menggunakan 50 dan 100 learner untuk mengetahui apakah penambahan jumlah model dasar memberikan perbaikan performa klasifikasi.
set.seed(22)
class_mod200 <- adabag::boosting(
Class ~ .,
data = data_train,
mfinal = 200,
control = rpart::rpart.control(
maxdepth = 1,
minsplit = 2,
cp = 0
)
)
print(class_mod200)## $formula
## Class ~ .
##
## $trees
## $trees[[1]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 182 benign (0.66666667 0.33333333)
## 2) Cell.size=1,2,3 383 25 benign (0.93472585 0.06527415) *
## 3) Cell.size=4,5,6,7,8,9,10 163 6 malignant (0.03680982 0.96319018) *
##
## $trees[[2]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 220 benign (0.59706960 0.40293040)
## 2) Bare.nuclei=1,2,3 336 29 benign (0.91369048 0.08630952) *
## 3) Bare.nuclei=4,5,6,7,8,9,10 210 19 malignant (0.09047619 0.90952381) *
##
## $trees[[3]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 238 benign (0.56410256 0.43589744)
## 2) Cell.size=1 225 6 benign (0.97333333 0.02666667) *
## 3) Cell.size=2,3,4,5,6,7,8,9,10 321 89 malignant (0.27725857 0.72274143) *
##
## $trees[[4]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 195 benign (0.6428571 0.3571429)
## 2) Cl.thickness=1,2,3,4,5,6 407 72 benign (0.8230958 0.1769042) *
## 3) Cl.thickness=7,8,9,10 139 16 malignant (0.1151079 0.8848921) *
##
## $trees[[5]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 232 benign (0.5750916 0.4249084)
## 2) Bare.nuclei=1,4,7,8 317 51 benign (0.8391167 0.1608833) *
## 3) Bare.nuclei=2,3,5,6,9,10 229 48 malignant (0.2096070 0.7903930) *
##
## $trees[[6]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 250 benign (0.5421245 0.4578755)
## 2) Normal.nucleoli=1,2,7,8 346 87 benign (0.7485549 0.2514451) *
## 3) Normal.nucleoli=3,4,5,6,9,10 200 37 malignant (0.1850000 0.8150000) *
##
## $trees[[7]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 271 malignant (0.4963370 0.5036630)
## 2) Bl.cromatin=1,2,3,4 361 110 benign (0.6952909 0.3047091) *
## 3) Bl.cromatin=5,6,7,8,9,10 185 20 malignant (0.1081081 0.8918919) *
##
## $trees[[8]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 249 malignant (0.4560440 0.5439560)
## 2) Cell.shape=1,2 179 41 benign (0.7709497 0.2290503) *
## 3) Cell.shape=3,4,5,6,7,8,9,10 367 111 malignant (0.3024523 0.6975477) *
##
## $trees[[9]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 270 benign (0.5054945 0.4945055)
## 2) Cl.thickness=1,2,3,4,5,6,7,8 460 184 benign (0.6000000 0.4000000) *
## 3) Cl.thickness=9,10 86 0 malignant (0.0000000 1.0000000) *
##
## $trees[[10]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 232 malignant (0.424908425 0.575091575)
## 2) Marg.adhesion=1,2,3,4,5 437 206 benign (0.528604119 0.471395881) *
## 3) Marg.adhesion=6,7,8,9,10 109 1 malignant (0.009174312 0.990825688) *
##
## $trees[[11]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 232 malignant (0.4249084 0.5750916)
## 2) Bare.nuclei=1,7 159 40 benign (0.7484277 0.2515723) *
## 3) Bare.nuclei=2,3,4,5,6,8,9,10 387 113 malignant (0.2919897 0.7080103) *
##
## $trees[[12]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 235 malignant (0.43040293 0.56959707)
## 2) Marg.adhesion=1,2,3,4,5 440 211 benign (0.52045455 0.47954545) *
## 3) Marg.adhesion=6,7,8,9,10 106 6 malignant (0.05660377 0.94339623) *
##
## $trees[[13]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 243 malignant (0.4450549 0.5549451)
## 2) Normal.nucleoli=2,3,7,8 148 39 benign (0.7364865 0.2635135) *
## 3) Normal.nucleoli=1,4,5,6,9,10 398 134 malignant (0.3366834 0.6633166) *
##
## $trees[[14]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 249 malignant (0.4560440 0.5439560)
## 2) Marg.adhesion=1,2,3,4,5 457 208 benign (0.5448578 0.4551422) *
## 3) Marg.adhesion=6,7,8,9,10 89 0 malignant (0.0000000 1.0000000) *
##
## $trees[[15]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 251 malignant (0.4597070 0.5402930)
## 2) Cl.thickness=1,2,3,4,5,6,7,8 470 219 benign (0.5340426 0.4659574) *
## 3) Cl.thickness=9,10 76 0 malignant (0.0000000 1.0000000) *
##
## $trees[[16]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 218 malignant (0.3992674 0.6007326)
## 2) Bl.cromatin=1,2,3,6 297 126 benign (0.5757576 0.4242424) *
## 3) Bl.cromatin=4,5,7,8,9,10 249 47 malignant (0.1887550 0.8112450) *
##
## $trees[[17]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 218 malignant (0.3992674 0.6007326)
## 2) Cl.thickness=1,2 70 7 benign (0.9000000 0.1000000) *
## 3) Cl.thickness=3,4,5,6,7,8,9,10 476 155 malignant (0.3256303 0.6743697) *
##
## $trees[[18]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 249 malignant (0.4560440 0.5439560)
## 2) Cl.thickness=1,2,3,4,5,6,7,8 473 224 benign (0.5264271 0.4735729) *
## 3) Cl.thickness=9,10 73 0 malignant (0.0000000 1.0000000) *
##
## $trees[[19]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 247 malignant (0.4523810 0.5476190)
## 2) Cl.thickness=1,2,3,4,5,6,7,8 481 234 benign (0.5135135 0.4864865) *
## 3) Cl.thickness=9,10 65 0 malignant (0.0000000 1.0000000) *
##
## $trees[[20]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 233 malignant (0.42673993 0.57326007)
## 2) Normal.nucleoli=1,2,4,5,6,7,8 440 216 benign (0.50909091 0.49090909) *
## 3) Normal.nucleoli=3,9,10 106 9 malignant (0.08490566 0.91509434) *
##
## $trees[[21]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 210 malignant (0.38461538 0.61538462)
## 2) Bl.cromatin=1,6 52 5 benign (0.90384615 0.09615385) *
## 3) Bl.cromatin=2,3,4,5,7,8,9,10 494 163 malignant (0.32995951 0.67004049) *
##
## $trees[[22]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 241 malignant (0.4413919 0.5586081)
## 2) Cell.shape=1 81 14 benign (0.8271605 0.1728395) *
## 3) Cell.shape=2,3,4,5,6,7,8,9,10 465 174 malignant (0.3741935 0.6258065) *
##
## $trees[[23]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 260 benign (0.52380952 0.47619048)
## 2) Marg.adhesion=1,2,3,4,5 485 203 benign (0.58144330 0.41855670) *
## 3) Marg.adhesion=6,7,8,9,10 61 4 malignant (0.06557377 0.93442623) *
##
## $trees[[24]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 240 malignant (0.4395604 0.5604396)
## 2) Bare.nuclei=1,3,7,8 265 103 benign (0.6113208 0.3886792) *
## 3) Bare.nuclei=2,4,5,6,9,10 281 78 malignant (0.2775801 0.7224199) *
##
## $trees[[25]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 237 malignant (0.4340659 0.5659341)
## 2) Cl.thickness=1,2,3,4,5,6,7,8 471 234 benign (0.5031847 0.4968153) *
## 3) Cl.thickness=9,10 75 0 malignant (0.0000000 1.0000000) *
##
## $trees[[26]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 231 malignant (0.4230769 0.5769231)
## 2) Normal.nucleoli=2,4,7 102 20 benign (0.8039216 0.1960784) *
## 3) Normal.nucleoli=1,3,5,6,8,9,10 444 149 malignant (0.3355856 0.6644144) *
##
## $trees[[27]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 245 malignant (0.4487179 0.5512821)
## 2) Cell.size=1,2,3,4 404 184 benign (0.5445545 0.4554455) *
## 3) Cell.size=5,6,7,8,9,10 142 25 malignant (0.1760563 0.8239437) *
##
## $trees[[28]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 260 malignant (0.4761905 0.5238095)
## 2) Normal.nucleoli=1,2,3,4,5,6,7,8 494 234 benign (0.5263158 0.4736842) *
## 3) Normal.nucleoli=9,10 52 0 malignant (0.0000000 1.0000000) *
##
## $trees[[29]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 220 malignant (0.4029304 0.5970696)
## 2) Bare.nuclei=1,2,3 168 64 benign (0.6190476 0.3809524) *
## 3) Bare.nuclei=4,5,6,7,8,9,10 378 116 malignant (0.3068783 0.6931217) *
##
## $trees[[30]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 254 malignant (0.4652015 0.5347985)
## 2) Normal.nucleoli=1,2,3,4,5,6,7,8 488 234 benign (0.5204918 0.4795082) *
## 3) Normal.nucleoli=9,10 58 0 malignant (0.0000000 1.0000000) *
##
## $trees[[31]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 240 malignant (0.43956044 0.56043956)
## 2) Normal.nucleoli=2 46 3 benign (0.93478261 0.06521739) *
## 3) Normal.nucleoli=1,3,4,5,6,7,8,9,10 500 197 malignant (0.39400000 0.60600000) *
##
## $trees[[32]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 248 malignant (0.4542125 0.5457875)
## 2) Cl.thickness=1,2,3,4,5,6,7,8 490 242 benign (0.5061224 0.4938776) *
## 3) Cl.thickness=9,10 56 0 malignant (0.0000000 1.0000000) *
##
## $trees[[33]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 264 malignant (0.4835165 0.5164835)
## 2) Normal.nucleoli=1,2,3,5,7,8 405 170 benign (0.5802469 0.4197531) *
## 3) Normal.nucleoli=4,6,9,10 141 29 malignant (0.2056738 0.7943262) *
##
## $trees[[34]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 256 malignant (0.46886447 0.53113553)
## 2) Marg.adhesion=1,2,3,4,5 486 234 benign (0.51851852 0.48148148) *
## 3) Marg.adhesion=6,7,8,9,10 60 4 malignant (0.06666667 0.93333333) *
##
## $trees[[35]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 229 malignant (0.4194139 0.5805861)
## 2) Normal.nucleoli=2,4,7 110 35 benign (0.6818182 0.3181818) *
## 3) Normal.nucleoli=1,3,5,6,8,9,10 436 154 malignant (0.3532110 0.6467890) *
##
## $trees[[36]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 249 malignant (0.4560440 0.5439560)
## 2) Cl.thickness=1,2,3,4,5,6,7,8 495 246 benign (0.5030303 0.4969697) *
## 3) Cl.thickness=9,10 51 0 malignant (0.0000000 1.0000000) *
##
## $trees[[37]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 237 malignant (0.4340659 0.5659341)
## 2) Mitoses=2 78 14 benign (0.8205128 0.1794872) *
## 3) Mitoses=1,3,4,6,7,8,10 468 173 malignant (0.3696581 0.6303419) *
##
## $trees[[38]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 245 malignant (0.4487179 0.5512821)
## 2) Cell.size=1,2,3,4 401 186 benign (0.5361596 0.4638404) *
## 3) Cell.size=5,6,7,8,9,10 145 30 malignant (0.2068966 0.7931034) *
##
## $trees[[39]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 235 malignant (0.4304029 0.5695971)
## 2) Normal.nucleoli=2,3,4,8 160 55 benign (0.6562500 0.3437500) *
## 3) Normal.nucleoli=1,5,6,7,9,10 386 130 malignant (0.3367876 0.6632124) *
##
## $trees[[40]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 251 benign (0.5402930 0.4597070)
## 2) Normal.nucleoli=1,2,3,4,5,6,7,8 505 210 benign (0.5841584 0.4158416) *
## 3) Normal.nucleoli=9,10 41 0 malignant (0.0000000 1.0000000) *
##
## $trees[[41]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 270 benign (0.5054945 0.4945055)
## 2) Cl.thickness=1,2,3,4,5,6,7,8 498 222 benign (0.5542169 0.4457831) *
## 3) Cl.thickness=9,10 48 0 malignant (0.0000000 1.0000000) *
##
## $trees[[42]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 253 malignant (0.4633700 0.5366300)
## 2) Cl.thickness=1,2,3,4,5,6 425 193 benign (0.5458824 0.4541176) *
## 3) Cl.thickness=7,8,9,10 121 21 malignant (0.1735537 0.8264463) *
##
## $trees[[43]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 248 malignant (0.45421245 0.54578755)
## 2) Marg.adhesion=1,2,3,4,5 480 237 benign (0.50625000 0.49375000) *
## 3) Marg.adhesion=6,7,8,9,10 66 5 malignant (0.07575758 0.92424242) *
##
## $trees[[44]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 226 malignant (0.4139194 0.5860806)
## 2) Bare.nuclei=1,2,3,7 198 81 benign (0.5909091 0.4090909) *
## 3) Bare.nuclei=4,5,6,8,9,10 348 109 malignant (0.3132184 0.6867816) *
##
## $trees[[45]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 226 malignant (0.41391941 0.58608059)
## 2) Bl.cromatin=1 36 3 benign (0.91666667 0.08333333) *
## 3) Bl.cromatin=2,3,4,5,6,7,8,10 510 193 malignant (0.37843137 0.62156863) *
##
## $trees[[46]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 258 malignant (0.4725275 0.5274725)
## 2) Epith.c.size=1,2,3,4,5 425 190 benign (0.5529412 0.4470588) *
## 3) Epith.c.size=6,7,8,9,10 121 23 malignant (0.1900826 0.8099174) *
##
## $trees[[47]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 245 malignant (0.4487179 0.5512821)
## 2) Normal.nucleoli=1,2,3,4,5,7,8 446 217 benign (0.5134529 0.4865471) *
## 3) Normal.nucleoli=6,9,10 100 16 malignant (0.1600000 0.8400000) *
##
## $trees[[48]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 250 malignant (0.4578755 0.5421245)
## 2) Normal.nucleoli=1,2,5,7,8 347 143 benign (0.5878963 0.4121037) *
## 3) Normal.nucleoli=3,4,6,9,10 199 46 malignant (0.2311558 0.7688442) *
##
## $trees[[49]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 244 malignant (0.4468864 0.5531136)
## 2) Cl.thickness=1,2,3,4,5,6,7,8 484 240 benign (0.5041322 0.4958678) *
## 3) Cl.thickness=9,10 62 0 malignant (0.0000000 1.0000000) *
##
## $trees[[50]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 235 malignant (0.4304029 0.5695971)
## 2) Normal.nucleoli=2,5,7 104 31 benign (0.7019231 0.2980769) *
## 3) Normal.nucleoli=1,3,4,6,8,9,10 442 162 malignant (0.3665158 0.6334842) *
##
## $trees[[51]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 259 malignant (0.4743590 0.5256410)
## 2) Cl.thickness=1,2,3,4,5,6,7,8 496 237 benign (0.5221774 0.4778226) *
## 3) Cl.thickness=9,10 50 0 malignant (0.0000000 1.0000000) *
##
## $trees[[52]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 240 malignant (0.4395604 0.5604396)
## 2) Marg.adhesion=1 204 81 benign (0.6029412 0.3970588) *
## 3) Marg.adhesion=2,3,4,5,6,7,8,9,10 342 117 malignant (0.3421053 0.6578947) *
##
## $trees[[53]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 249 malignant (0.4560440 0.5439560)
## 2) Bare.nuclei=1,2,3,8 250 98 benign (0.6080000 0.3920000) *
## 3) Bare.nuclei=4,5,6,7,9,10 296 97 malignant (0.3277027 0.6722973) *
##
## $trees[[54]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 266 malignant (0.4871795 0.5128205)
## 2) Cl.thickness=1,2,3,4,5,6,7,8 504 238 benign (0.5277778 0.4722222) *
## 3) Cl.thickness=9,10 42 0 malignant (0.0000000 1.0000000) *
##
## $trees[[55]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 267 malignant (0.4890110 0.5109890)
## 2) Cl.thickness=1,2,3,4,5,6,7,8 504 237 benign (0.5297619 0.4702381) *
## 3) Cl.thickness=9,10 42 0 malignant (0.0000000 1.0000000) *
##
## $trees[[56]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 244 malignant (0.4468864 0.5531136)
## 2) Mitoses=2 63 10 benign (0.8412698 0.1587302) *
## 3) Mitoses=1,3,4,5,8,10 483 191 malignant (0.3954451 0.6045549) *
##
## $trees[[57]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 266 malignant (0.4871795 0.5128205)
## 2) Normal.nucleoli=1,2,3,4,5,6,7,8 501 235 benign (0.5309381 0.4690619) *
## 3) Normal.nucleoli=9,10 45 0 malignant (0.0000000 1.0000000) *
##
## $trees[[58]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 259 malignant (0.4743590 0.5256410)
## 2) Normal.nucleoli=1,2,3,4,5,6,7,8 509 250 benign (0.5088409 0.4911591) *
## 3) Normal.nucleoli=9,10 37 0 malignant (0.0000000 1.0000000) *
##
## $trees[[59]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 261 malignant (0.4780220 0.5219780)
## 2) Cl.thickness=1,2,3,4,5,6,7,8 505 244 benign (0.5168317 0.4831683) *
## 3) Cl.thickness=9,10 41 0 malignant (0.0000000 1.0000000) *
##
## $trees[[60]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 238 malignant (0.4358974 0.5641026)
## 2) Normal.nucleoli=2,5,7 96 26 benign (0.7291667 0.2708333) *
## 3) Normal.nucleoli=1,3,4,6,8,9,10 450 168 malignant (0.3733333 0.6266667) *
##
## $trees[[61]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 255 malignant (0.4670330 0.5329670)
## 2) Cl.thickness=1,2,3,4,5,6,7,8 500 245 benign (0.5100000 0.4900000) *
## 3) Cl.thickness=9,10 46 0 malignant (0.0000000 1.0000000) *
##
## $trees[[62]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 252 malignant (0.4615385 0.5384615)
## 2) Epith.c.size=1,2,3,4,5,6,7 501 249 benign (0.5029940 0.4970060) *
## 3) Epith.c.size=8,9,10 45 0 malignant (0.0000000 1.0000000) *
##
## $trees[[63]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 254 malignant (0.4652015 0.5347985)
## 2) Cl.thickness=1,2,3,4,5,6,7,8 505 251 benign (0.5029703 0.4970297) *
## 3) Cl.thickness=9,10 41 0 malignant (0.0000000 1.0000000) *
##
## $trees[[64]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 228 malignant (0.4175824 0.5824176)
## 2) Cell.size=4,5,6,7,8,9,10 241 106 benign (0.5601660 0.4398340) *
## 3) Cell.size=1,2,3 305 93 malignant (0.3049180 0.6950820) *
##
## $trees[[65]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 260 malignant (0.4761905 0.5238095)
## 2) Cell.size=1,2,3,4 402 178 benign (0.5572139 0.4427861) *
## 3) Cell.size=5,6,7,8,9,10 144 36 malignant (0.2500000 0.7500000) *
##
## $trees[[66]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 230 malignant (0.42124542 0.57875458)
## 2) Bl.cromatin=1 36 2 benign (0.94444444 0.05555556) *
## 3) Bl.cromatin=2,3,4,5,6,7,8,9,10 510 196 malignant (0.38431373 0.61568627) *
##
## $trees[[67]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 259 malignant (0.4743590 0.5256410)
## 2) Normal.nucleoli=1,2,3,4,5,6,7,8 506 247 benign (0.5118577 0.4881423) *
## 3) Normal.nucleoli=9,10 40 0 malignant (0.0000000 1.0000000) *
##
## $trees[[68]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 252 malignant (0.4615385 0.5384615)
## 2) Bare.nuclei=1,2,3,7,8 317 136 benign (0.5709779 0.4290221) *
## 3) Bare.nuclei=4,5,6,9,10 229 71 malignant (0.3100437 0.6899563) *
##
## $trees[[69]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 271 benign (0.5036630 0.4963370)
## 2) Epith.c.size=1,2,3,4,5,6,7 499 224 benign (0.5511022 0.4488978) *
## 3) Epith.c.size=8,9,10 47 0 malignant (0.0000000 1.0000000) *
##
## $trees[[70]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 244 malignant (0.4468864 0.5531136)
## 2) Bare.nuclei=1,3,5,7 304 126 benign (0.5855263 0.4144737) *
## 3) Bare.nuclei=2,4,6,8,9,10 242 66 malignant (0.2727273 0.7272727) *
##
## $trees[[71]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 244 malignant (0.4468864 0.5531136)
## 2) Cl.thickness=1,2,3,4,5,6 435 212 benign (0.5126437 0.4873563) *
## 3) Cl.thickness=7,8,9,10 111 21 malignant (0.1891892 0.8108108) *
##
## $trees[[72]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 261 malignant (0.4780220 0.5219780)
## 2) Epith.c.size=1,2,3,4,5,6,7 506 245 benign (0.5158103 0.4841897) *
## 3) Epith.c.size=8,9,10 40 0 malignant (0.0000000 1.0000000) *
##
## $trees[[73]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 199 benign (0.63553114 0.36446886)
## 2) Cell.size=1,2,3 372 30 benign (0.91935484 0.08064516) *
## 3) Cell.size=4,5,6,7,8,9,10 174 5 malignant (0.02873563 0.97126437) *
##
## $trees[[74]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 240 benign (0.56043956 0.43956044)
## 2) Bl.cromatin=1,2,3 334 45 benign (0.86526946 0.13473054) *
## 3) Bl.cromatin=4,5,6,7,8,9,10 212 17 malignant (0.08018868 0.91981132) *
##
## $trees[[75]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 264 benign (0.51648352 0.48351648)
## 2) Bare.nuclei=1,2,3,8 309 50 benign (0.83818770 0.16181230) *
## 3) Bare.nuclei=4,5,6,7,9,10 237 23 malignant (0.09704641 0.90295359) *
##
## $trees[[76]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 263 benign (0.5183150 0.4816850)
## 2) Cell.shape=1,2 233 22 benign (0.9055794 0.0944206) *
## 3) Cell.shape=3,4,5,6,7,8,9,10 313 72 malignant (0.2300319 0.7699681) *
##
## $trees[[77]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 236 benign (0.5677656 0.4322344)
## 2) Bare.nuclei=1,3,4,7 319 50 benign (0.8432602 0.1567398) *
## 3) Bare.nuclei=2,5,6,8,9,10 227 41 malignant (0.1806167 0.8193833) *
##
## $trees[[78]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 242 benign (0.5567766 0.4432234)
## 2) Cl.thickness=1,2,3,4,5,6 363 92 benign (0.7465565 0.2534435) *
## 3) Cl.thickness=7,8,9,10 183 33 malignant (0.1803279 0.8196721) *
##
## $trees[[79]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 268 malignant (0.4908425 0.5091575)
## 2) Normal.nucleoli=1,2,7,8 347 107 benign (0.6916427 0.3083573) *
## 3) Normal.nucleoli=3,4,5,6,9,10 199 28 malignant (0.1407035 0.8592965) *
##
## $trees[[80]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 261 malignant (0.4780220 0.5219780)
## 2) Bare.nuclei=1,7 213 53 benign (0.7511737 0.2488263) *
## 3) Bare.nuclei=2,3,4,5,6,8,9,10 333 101 malignant (0.3033033 0.6966967) *
##
## $trees[[81]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 270 malignant (0.49450549 0.50549451)
## 2) Marg.adhesion=1,2,3,4,5 447 180 benign (0.59731544 0.40268456) *
## 3) Marg.adhesion=6,7,8,9,10 99 3 malignant (0.03030303 0.96969697) *
##
## $trees[[82]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 226 malignant (0.4139194 0.5860806)
## 2) Cl.thickness=1,2,3,4,5,6,7,8 441 215 benign (0.5124717 0.4875283) *
## 3) Cl.thickness=9,10 105 0 malignant (0.0000000 1.0000000) *
##
## $trees[[83]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 230 malignant (0.4212454 0.5787546)
## 2) Cell.size=1 112 20 benign (0.8214286 0.1785714) *
## 3) Cell.size=2,3,4,5,6,7,8,9,10 434 138 malignant (0.3179724 0.6820276) *
##
## $trees[[84]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 243 malignant (0.4450549 0.5549451)
## 2) Cl.thickness=1,2,3,4,5,6,7,8 458 215 benign (0.5305677 0.4694323) *
## 3) Cl.thickness=9,10 88 0 malignant (0.0000000 1.0000000) *
##
## $trees[[85]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 234 malignant (0.4285714 0.5714286)
## 2) Normal.nucleoli=1,2,3,7,8 384 174 benign (0.5468750 0.4531250) *
## 3) Normal.nucleoli=4,5,6,9,10 162 24 malignant (0.1481481 0.8518519) *
##
## $trees[[86]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 207 malignant (0.3791209 0.6208791)
## 2) Normal.nucleoli=2,3,4,5,7 169 65 benign (0.6153846 0.3846154) *
## 3) Normal.nucleoli=1,6,8,9,10 377 103 malignant (0.2732095 0.7267905) *
##
## $trees[[87]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 238 malignant (0.4358974 0.5641026)
## 2) Cl.thickness=1,2,3 109 18 benign (0.8348624 0.1651376) *
## 3) Cl.thickness=4,5,6,7,8,9,10 437 147 malignant (0.3363844 0.6636156) *
##
## $trees[[88]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 260 malignant (0.4761905 0.5238095)
## 2) Cl.thickness=1,2,3,4,5,6,7,8 472 212 benign (0.5508475 0.4491525) *
## 3) Cl.thickness=9,10 74 0 malignant (0.0000000 1.0000000) *
##
## $trees[[89]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 237 malignant (0.43406593 0.56593407)
## 2) Marg.adhesion=1,2,3,4,5 464 228 benign (0.50862069 0.49137931) *
## 3) Marg.adhesion=6,7,8,9,10 82 1 malignant (0.01219512 0.98780488) *
##
## $trees[[90]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 232 malignant (0.4249084 0.5750916)
## 2) Bare.nuclei=1,2,7,8 267 108 benign (0.5955056 0.4044944) *
## 3) Bare.nuclei=3,4,5,6,9,10 279 73 malignant (0.2616487 0.7383513) *
##
## $trees[[91]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 204 malignant (0.3736264 0.6263736)
## 2) Normal.nucleoli=2,7 70 15 benign (0.7857143 0.2142857) *
## 3) Normal.nucleoli=1,3,4,5,6,8,9,10 476 149 malignant (0.3130252 0.6869748) *
##
## $trees[[92]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 258 malignant (0.4725275 0.5274725)
## 2) Cl.thickness=1,2,3,4,5,6,7,8 485 227 benign (0.5319588 0.4680412) *
## 3) Cl.thickness=9,10 61 0 malignant (0.0000000 1.0000000) *
##
## $trees[[93]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 233 malignant (0.4267399 0.5732601)
## 2) Cell.size=1,2,3,4 396 185 benign (0.5328283 0.4671717) *
## 3) Cell.size=5,6,7,8,9,10 150 22 malignant (0.1466667 0.8533333) *
##
## $trees[[94]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 220 malignant (0.4029304 0.5970696)
## 2) Bare.nuclei=1,3,7 203 81 benign (0.6009852 0.3990148) *
## 3) Bare.nuclei=2,4,5,6,8,9,10 343 98 malignant (0.2857143 0.7142857) *
##
## $trees[[95]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 229 malignant (0.4194139 0.5805861)
## 2) Normal.nucleoli=2,7,8 117 28 benign (0.7606838 0.2393162) *
## 3) Normal.nucleoli=1,3,4,5,6,9,10 429 140 malignant (0.3263403 0.6736597) *
##
## $trees[[96]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 235 malignant (0.4304029 0.5695971)
## 2) Bl.cromatin=1,4,6 128 36 benign (0.7187500 0.2812500) *
## 3) Bl.cromatin=2,3,5,7,8,9,10 418 143 malignant (0.3421053 0.6578947) *
##
## $trees[[97]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 268 benign (0.50915751 0.49084249)
## 2) Marg.adhesion=1,2,3,4,5 476 201 benign (0.57773109 0.42226891) *
## 3) Marg.adhesion=6,7,8,9,10 70 3 malignant (0.04285714 0.95714286) *
##
## $trees[[98]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 265 malignant (0.4853480 0.5146520)
## 2) Cl.thickness=1,2,3,4,5,6,7,8 486 221 benign (0.5452675 0.4547325) *
## 3) Cl.thickness=9,10 60 0 malignant (0.0000000 1.0000000) *
##
## $trees[[99]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 250 malignant (0.4578755 0.5421245)
## 2) Epith.c.size=1,2,3,4,5 397 170 benign (0.5717884 0.4282116) *
## 3) Epith.c.size=6,7,8,9,10 149 23 malignant (0.1543624 0.8456376) *
##
## $trees[[100]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 229 malignant (0.4194139 0.5805861)
## 2) Cell.size=1,2,3,4 402 199 benign (0.5049751 0.4950249) *
## 3) Cell.size=5,6,7,8,9,10 144 26 malignant (0.1805556 0.8194444) *
##
## $trees[[101]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 209 malignant (0.38278388 0.61721612)
## 2) Normal.nucleoli=2 33 3 benign (0.90909091 0.09090909) *
## 3) Normal.nucleoli=1,3,4,5,6,7,8,9,10 513 179 malignant (0.34892788 0.65107212) *
##
## $trees[[102]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 251 malignant (0.4597070 0.5402930)
## 2) Cl.thickness=1,2,3,4,5,6,7,8 495 244 benign (0.5070707 0.4929293) *
## 3) Cl.thickness=9,10 51 0 malignant (0.0000000 1.0000000) *
##
## $trees[[103]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 224 malignant (0.4102564 0.5897436)
## 2) Bare.nuclei=1,2,3,7,8 298 134 benign (0.5503356 0.4496644) *
## 3) Bare.nuclei=4,5,6,9,10 248 60 malignant (0.2419355 0.7580645) *
##
## $trees[[104]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 249 malignant (0.4560440 0.5439560)
## 2) Normal.nucleoli=1,2,3,4,5,6,7,8 490 241 benign (0.5081633 0.4918367) *
## 3) Normal.nucleoli=9,10 56 0 malignant (0.0000000 1.0000000) *
##
## $trees[[105]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 239 malignant (0.4377289 0.5622711)
## 2) Normal.nucleoli=1,2,7,8 315 136 benign (0.5682540 0.4317460) *
## 3) Normal.nucleoli=3,4,5,6,9,10 231 60 malignant (0.2597403 0.7402597) *
##
## $trees[[106]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 254 malignant (0.4652015 0.5347985)
## 2) Bl.cromatin=1,2,3,4,5,6 451 209 benign (0.5365854 0.4634146) *
## 3) Bl.cromatin=7,8,9,10 95 12 malignant (0.1263158 0.8736842) *
##
## $trees[[107]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 266 malignant (0.4871795 0.5128205)
## 2) Normal.nucleoli=1,2,3,4,5,6,7,8 500 234 benign (0.5320000 0.4680000) *
## 3) Normal.nucleoli=9,10 46 0 malignant (0.0000000 1.0000000) *
##
## $trees[[108]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 188 benign (0.65567766 0.34432234)
## 2) Bare.nuclei=1,2,3 357 18 benign (0.94957983 0.05042017) *
## 3) Bare.nuclei=4,5,6,7,8,9,10 189 19 malignant (0.10052910 0.89947090) *
##
## $trees[[109]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 216 benign (0.60439560 0.39560440)
## 2) Cell.shape=1,2,3 328 24 benign (0.92682927 0.07317073) *
## 3) Cell.shape=4,5,6,7,8,9,10 218 26 malignant (0.11926606 0.88073394) *
##
## $trees[[110]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 217 benign (0.60256410 0.39743590)
## 2) Cl.thickness=1,2,3,4,5,6 379 64 benign (0.83113456 0.16886544) *
## 3) Cl.thickness=7,8,9,10 167 14 malignant (0.08383234 0.91616766) *
##
## $trees[[111]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 260 benign (0.52380952 0.47619048)
## 2) Cell.size=1 207 9 benign (0.95652174 0.04347826) *
## 3) Cell.size=2,3,4,5,6,7,8,9,10 339 88 malignant (0.25958702 0.74041298) *
##
## $trees[[112]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 246 benign (0.5494505 0.4505495)
## 2) Bl.cromatin=1,2,3,4 368 91 benign (0.7527174 0.2472826) *
## 3) Bl.cromatin=5,6,7,8,9,10 178 23 malignant (0.1292135 0.8707865) *
##
## $trees[[113]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 270 malignant (0.4945055 0.5054945)
## 2) Normal.nucleoli=1,2,7,8 339 93 benign (0.7256637 0.2743363) *
## 3) Normal.nucleoli=3,4,5,6,9,10 207 24 malignant (0.1159420 0.8840580) *
##
## $trees[[114]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 247 malignant (0.4523810 0.5476190)
## 2) Bare.nuclei=1,7,8 239 57 benign (0.7615063 0.2384937) *
## 3) Bare.nuclei=2,3,4,5,6,9,10 307 65 malignant (0.2117264 0.7882736) *
##
## $trees[[115]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 266 benign (0.5128205 0.4871795)
## 2) Cell.size=1,2,3,4 350 99 benign (0.7171429 0.2828571) *
## 3) Cell.size=5,6,7,8,9,10 196 29 malignant (0.1479592 0.8520408) *
##
## $trees[[116]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 252 malignant (0.461538462 0.538461538)
## 2) Marg.adhesion=1,2,3,4,5 423 172 benign (0.593380615 0.406619385) *
## 3) Marg.adhesion=6,7,8,9,10 123 1 malignant (0.008130081 0.991869919) *
##
## $trees[[117]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 202 malignant (0.3699634 0.6300366)
## 2) Cell.shape=1 93 18 benign (0.8064516 0.1935484) *
## 3) Cell.shape=2,3,4,5,6,7,8,9,10 453 127 malignant (0.2803532 0.7196468) *
##
## $trees[[118]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 229 malignant (0.4194139 0.5805861)
## 2) Cl.thickness=1,2,3,4,5,6,7,8 447 218 benign (0.5123043 0.4876957) *
## 3) Cl.thickness=9,10 99 0 malignant (0.0000000 1.0000000) *
##
## $trees[[119]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 241 malignant (0.4413919 0.5586081)
## 2) Marg.adhesion=1,2,3,4,5 446 208 benign (0.5336323 0.4663677) *
## 3) Marg.adhesion=6,7,8,9,10 100 3 malignant (0.0300000 0.9700000) *
##
## $trees[[120]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 214 malignant (0.3919414 0.6080586)
## 2) Bare.nuclei=1,2,4,7,8 321 146 benign (0.5451713 0.4548287) *
## 3) Bare.nuclei=3,5,6,9,10 225 39 malignant (0.1733333 0.8266667) *
##
## $trees[[121]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 190 malignant (0.3479853 0.6520147)
## 2) Normal.nucleoli=2,7 66 10 benign (0.8484848 0.1515152) *
## 3) Normal.nucleoli=1,3,4,5,6,8,9,10 480 134 malignant (0.2791667 0.7208333) *
##
## $trees[[122]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 252 malignant (0.46153846 0.53846154)
## 2) Marg.adhesion=1,2,3,4,5 449 204 benign (0.54565702 0.45434298) *
## 3) Marg.adhesion=6,7,8,9,10 97 7 malignant (0.07216495 0.92783505) *
##
## $trees[[123]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 245 malignant (0.4487179 0.5512821)
## 2) Cell.size=1,2,3,4 398 175 benign (0.5603015 0.4396985) *
## 3) Cell.size=5,6,7,8,9,10 148 22 malignant (0.1486486 0.8513514) *
##
## $trees[[124]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 209 malignant (0.3827839 0.6172161)
## 2) Bare.nuclei=1,3,7 182 70 benign (0.6153846 0.3846154) *
## 3) Bare.nuclei=2,4,5,6,8,9,10 364 97 malignant (0.2664835 0.7335165) *
##
## $trees[[125]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 230 malignant (0.42124542 0.57875458)
## 2) Normal.nucleoli=1,2,3,4,5,7,8 422 199 benign (0.52843602 0.47156398) *
## 3) Normal.nucleoli=6,9,10 124 7 malignant (0.05645161 0.94354839) *
##
## $trees[[126]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 218 malignant (0.3992674 0.6007326)
## 2) Bl.cromatin=1,3 205 70 benign (0.6585366 0.3414634) *
## 3) Bl.cromatin=2,4,5,6,7,8,9,10 341 83 malignant (0.2434018 0.7565982) *
##
## $trees[[127]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 232 malignant (0.4249084 0.5750916)
## 2) Epith.c.size=1,2,3,4,5 403 189 benign (0.5310174 0.4689826) *
## 3) Epith.c.size=6,7,8,9,10 143 18 malignant (0.1258741 0.8741259) *
##
## $trees[[128]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 210 malignant (0.3846154 0.6153846)
## 2) Cl.thickness=1 34 0 benign (1.0000000 0.0000000) *
## 3) Cl.thickness=2,3,4,5,6,7,8,9,10 512 176 malignant (0.3437500 0.6562500) *
##
## $trees[[129]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 261 malignant (0.47802198 0.52197802)
## 2) Normal.nucleoli=1,2,3,4,5,7,8 455 202 benign (0.55604396 0.44395604) *
## 3) Normal.nucleoli=6,9,10 91 8 malignant (0.08791209 0.91208791) *
##
## $trees[[130]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 237 malignant (0.4340659 0.5659341)
## 2) Cl.thickness=1,2,3,4,5,6,7,8 457 220 benign (0.5185996 0.4814004) *
## 3) Cl.thickness=9,10 89 0 malignant (0.0000000 1.0000000) *
##
## $trees[[131]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 191 malignant (0.3498168 0.6501832)
## 2) Normal.nucleoli=2,4,7,8 137 55 benign (0.5985401 0.4014599) *
## 3) Normal.nucleoli=1,3,5,6,9,10 409 109 malignant (0.2665037 0.7334963) *
##
## $trees[[132]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 247 malignant (0.45238095 0.54761905)
## 2) Marg.adhesion=1,2,3,4,5 454 213 benign (0.53083700 0.46916300) *
## 3) Marg.adhesion=6,7,8,9,10 92 6 malignant (0.06521739 0.93478261) *
##
## $trees[[133]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 231 malignant (0.4230769 0.5769231)
## 2) Cl.thickness=1,2,3 91 20 benign (0.7802198 0.2197802) *
## 3) Cl.thickness=4,5,6,7,8,9,10 455 160 malignant (0.3516484 0.6483516) *
##
## $trees[[134]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 262 malignant (0.4798535 0.5201465)
## 2) Cl.thickness=1,2,3,4,5,6,7,8 462 200 benign (0.5670996 0.4329004) *
## 3) Cl.thickness=9,10 84 0 malignant (0.0000000 1.0000000) *
##
## $trees[[135]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 237 malignant (0.4340659 0.5659341)
## 2) Cell.size=1,2,3,4 384 175 benign (0.5442708 0.4557292) *
## 3) Cell.size=5,6,7,8,9,10 162 28 malignant (0.1728395 0.8271605) *
##
## $trees[[136]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 227 malignant (0.4157509 0.5842491)
## 2) Normal.nucleoli=2,7,8 124 36 benign (0.7096774 0.2903226) *
## 3) Normal.nucleoli=1,3,4,5,6,9,10 422 139 malignant (0.3293839 0.6706161) *
##
## $trees[[137]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 238 malignant (0.4358974 0.5641026)
## 2) Cl.thickness=1,2,3,4,5,6,7,8 474 236 benign (0.5021097 0.4978903) *
## 3) Cl.thickness=9,10 72 0 malignant (0.0000000 1.0000000) *
##
## $trees[[138]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 236 malignant (0.4322344 0.5677656)
## 2) Epith.c.size=1,2,3,4,5 426 209 benign (0.5093897 0.4906103) *
## 3) Epith.c.size=6,7,8,9,10 120 19 malignant (0.1583333 0.8416667) *
##
## $trees[[139]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 202 malignant (0.3699634 0.6300366)
## 2) Bl.cromatin=1 47 5 benign (0.8936170 0.1063830) *
## 3) Bl.cromatin=2,3,4,5,6,7,8,9,10 499 160 malignant (0.3206413 0.6793587) *
##
## $trees[[140]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 245 malignant (0.4487179 0.5512821)
## 2) Cl.thickness=1,2,3,4,5,6,7,8 483 238 benign (0.5072464 0.4927536) *
## 3) Cl.thickness=9,10 63 0 malignant (0.0000000 1.0000000) *
##
## $trees[[141]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 234 malignant (0.4285714 0.5714286)
## 2) Normal.nucleoli=2,3,5,7,8 168 60 benign (0.6428571 0.3571429) *
## 3) Normal.nucleoli=1,4,6,9,10 378 126 malignant (0.3333333 0.6666667) *
##
## $trees[[142]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 256 malignant (0.4688645 0.5311355)
## 2) Bare.nuclei=1,3 150 42 benign (0.7200000 0.2800000) *
## 3) Bare.nuclei=2,4,5,6,7,8,9,10 396 148 malignant (0.3737374 0.6262626) *
##
## $trees[[143]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 261 malignant (0.4780220 0.5219780)
## 2) Marg.adhesion=1,2,3,4,5 461 210 benign (0.5444685 0.4555315) *
## 3) Marg.adhesion=6,7,8,9,10 85 10 malignant (0.1176471 0.8823529) *
##
## $trees[[144]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 245 malignant (0.4487179 0.5512821)
## 2) Bl.cromatin=1,2,3,6 320 134 benign (0.5812500 0.4187500) *
## 3) Bl.cromatin=4,5,7,8,9,10 226 59 malignant (0.2610619 0.7389381) *
##
## $trees[[145]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 243 malignant (0.4450549 0.5549451)
## 2) Cl.thickness=1,2,3,4,5,6,7,8 465 222 benign (0.5225806 0.4774194) *
## 3) Cl.thickness=9,10 81 0 malignant (0.0000000 1.0000000) *
##
## $trees[[146]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 246 malignant (0.45054945 0.54945055)
## 2) Normal.nucleoli=1,2,3,4,5,7,8 465 226 benign (0.51397849 0.48602151) *
## 3) Normal.nucleoli=6,9,10 81 7 malignant (0.08641975 0.91358025) *
##
## $trees[[147]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 244 malignant (0.4468864 0.5531136)
## 2) Cl.thickness=1,2,3,4,5,6,7,8 487 243 benign (0.5010267 0.4989733) *
## 3) Cl.thickness=9,10 59 0 malignant (0.0000000 1.0000000) *
##
## $trees[[148]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 220 malignant (0.40293040 0.59706960)
## 2) Bl.cromatin=1 42 3 benign (0.92857143 0.07142857) *
## 3) Bl.cromatin=2,3,4,5,6,7,8,9,10 504 181 malignant (0.35912698 0.64087302) *
##
## $trees[[149]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 265 malignant (0.4853480 0.5146520)
## 2) Cl.thickness=1,2,3,4,5,6,7,8 491 226 benign (0.5397149 0.4602851) *
## 3) Cl.thickness=9,10 55 0 malignant (0.0000000 1.0000000) *
##
## $trees[[150]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 246 malignant (0.4505495 0.5494505)
## 2) Marg.adhesion=1 215 81 benign (0.6232558 0.3767442) *
## 3) Marg.adhesion=2,3,4,5,6,7,8,9,10 331 112 malignant (0.3383686 0.6616314) *
##
## $trees[[151]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 261 malignant (0.4780220 0.5219780)
## 2) Cl.thickness=1,2,3,4,5,6,7,8 491 230 benign (0.5315682 0.4684318) *
## 3) Cl.thickness=9,10 55 0 malignant (0.0000000 1.0000000) *
##
## $trees[[152]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 220 malignant (0.4029304 0.5970696)
## 2) Normal.nucleoli=2,7 67 16 benign (0.7611940 0.2388060) *
## 3) Normal.nucleoli=1,3,4,5,6,8,9,10 479 169 malignant (0.3528184 0.6471816) *
##
## $trees[[153]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 271 malignant (0.4963370 0.5036630)
## 2) Cl.thickness=1,2,3,4,5,6,7,8 498 227 benign (0.5441767 0.4558233) *
## 3) Cl.thickness=9,10 48 0 malignant (0.0000000 1.0000000) *
##
## $trees[[154]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 230 malignant (0.4212454 0.5787546)
## 2) Bare.nuclei=1,2,3,7,8 318 151 benign (0.5251572 0.4748428) *
## 3) Bare.nuclei=4,5,6,9,10 228 63 malignant (0.2763158 0.7236842) *
##
## $trees[[155]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 245 malignant (0.4487179 0.5512821)
## 2) Epith.c.size=1,2,3,4,5 418 199 benign (0.5239234 0.4760766) *
## 3) Epith.c.size=6,7,8,9,10 128 26 malignant (0.2031250 0.7968750) *
##
## $trees[[156]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 231 malignant (0.4230769 0.5769231)
## 2) Normal.nucleoli=2,5 53 9 benign (0.8301887 0.1698113) *
## 3) Normal.nucleoli=1,3,4,6,7,8,9,10 493 187 malignant (0.3793103 0.6206897) *
##
## $trees[[157]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 234 malignant (0.4285714 0.5714286)
## 2) Epith.c.size=1,2,3,4,5 411 205 benign (0.5012165 0.4987835) *
## 3) Epith.c.size=6,7,8,9,10 135 28 malignant (0.2074074 0.7925926) *
##
## $trees[[158]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 271 malignant (0.4963370 0.5036630)
## 2) Cl.thickness=1,2,3,4,5,6,7,8 504 233 benign (0.5376984 0.4623016) *
## 3) Cl.thickness=9,10 42 0 malignant (0.0000000 1.0000000) *
##
## $trees[[159]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 237 malignant (0.4340659 0.5659341)
## 2) Cell.size=1,2,3,4 383 182 benign (0.5248042 0.4751958) *
## 3) Cell.size=5,6,7,8,9,10 163 36 malignant (0.2208589 0.7791411) *
##
## $trees[[160]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 257 malignant (0.4706960 0.5293040)
## 2) Cl.thickness=1,2,3,4,5,6,7,8 509 252 benign (0.5049116 0.4950884) *
## 3) Cl.thickness=9,10 37 0 malignant (0.0000000 1.0000000) *
##
## $trees[[161]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 251 malignant (0.4597070 0.5402930)
## 2) Normal.nucleoli=1,2,3,4,5,6,7,8 500 249 benign (0.5020000 0.4980000) *
## 3) Normal.nucleoli=9,10 46 0 malignant (0.0000000 1.0000000) *
##
## $trees[[162]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 230 malignant (0.4212454 0.5787546)
## 2) Normal.nucleoli=2,7 48 7 benign (0.8541667 0.1458333) *
## 3) Normal.nucleoli=1,3,4,5,6,8,9,10 498 189 malignant (0.3795181 0.6204819) *
##
## $trees[[163]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 237 malignant (0.4340659 0.5659341)
## 2) Bare.nuclei=1,2,3 186 71 benign (0.6182796 0.3817204) *
## 3) Bare.nuclei=4,5,6,7,8,9,10 360 122 malignant (0.3388889 0.6611111) *
##
## $trees[[164]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 269 benign (0.5073260 0.4926740)
## 2) Normal.nucleoli=1,2,3,4,5,6,7,8 503 226 benign (0.5506958 0.4493042) *
## 3) Normal.nucleoli=9,10 43 0 malignant (0.0000000 1.0000000) *
##
## $trees[[165]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 256 malignant (0.4688645 0.5311355)
## 2) Cl.thickness=1,2,3,4,5,6,7,8 490 234 benign (0.5224490 0.4775510) *
## 3) Cl.thickness=9,10 56 0 malignant (0.0000000 1.0000000) *
##
## $trees[[166]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 259 malignant (0.4743590 0.5256410)
## 2) Normal.nucleoli=1,2,3,4,5,7,8 449 207 benign (0.5389755 0.4610245) *
## 3) Normal.nucleoli=6,9,10 97 17 malignant (0.1752577 0.8247423) *
##
## $trees[[167]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 264 malignant (0.4835165 0.5164835)
## 2) Cl.thickness=1,2,3,4,5,6,7,8 505 241 benign (0.5227723 0.4772277) *
## 3) Cl.thickness=9,10 41 0 malignant (0.0000000 1.0000000) *
##
## $trees[[168]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 261 malignant (0.4780220 0.5219780)
## 2) Normal.nucleoli=1,2,3,4,5,6,7,8 511 250 benign (0.5107632 0.4892368) *
## 3) Normal.nucleoli=9,10 35 0 malignant (0.0000000 1.0000000) *
##
## $trees[[169]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 192 benign (0.64835165 0.35164835)
## 2) Cell.shape=1,2,3 366 23 benign (0.93715847 0.06284153) *
## 3) Cell.shape=4,5,6,7,8,9,10 180 11 malignant (0.06111111 0.93888889) *
##
## $trees[[170]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 222 benign (0.59340659 0.40659341)
## 2) Bare.nuclei=1 289 9 benign (0.96885813 0.03114187) *
## 3) Bare.nuclei=2,3,4,5,6,7,8,9,10 257 44 malignant (0.17120623 0.82879377) *
##
## $trees[[171]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 171 benign (0.6868132 0.3131868)
## 2) Bare.nuclei=1,2,3,4,7,8 391 42 benign (0.8925831 0.1074169) *
## 3) Bare.nuclei=5,6,9,10 155 26 malignant (0.1677419 0.8322581) *
##
## $trees[[172]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 237 benign (0.5659341 0.4340659)
## 2) Bl.cromatin=1,2,3,6 333 57 benign (0.8288288 0.1711712) *
## 3) Bl.cromatin=4,5,7,8,9,10 213 33 malignant (0.1549296 0.8450704) *
##
## $trees[[173]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 232 benign (0.57509158 0.42490842)
## 2) Cl.thickness=1,2,3,4,5,6 376 78 benign (0.79255319 0.20744681) *
## 3) Cl.thickness=7,8,9,10 170 16 malignant (0.09411765 0.90588235) *
##
## $trees[[174]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 271 malignant (0.49633700 0.50366300)
## 2) Cell.size=1,2,3,4 363 106 benign (0.70798898 0.29201102) *
## 3) Cell.size=5,6,7,8,9,10 183 14 malignant (0.07650273 0.92349727) *
##
## $trees[[175]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 247 malignant (0.4523810 0.5476190)
## 2) Normal.nucleoli=1,2,7,8 326 104 benign (0.6809816 0.3190184) *
## 3) Normal.nucleoli=3,4,5,6,9,10 220 25 malignant (0.1136364 0.8863636) *
##
## $trees[[176]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 217 malignant (0.3974359 0.6025641)
## 2) Cell.shape=1 116 19 benign (0.8362069 0.1637931) *
## 3) Cell.shape=2,3,4,5,6,7,8,9,10 430 120 malignant (0.2790698 0.7209302) *
##
## $trees[[177]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 235 malignant (0.4304029 0.5695971)
## 2) Cell.size=1 151 30 benign (0.8013245 0.1986755) *
## 3) Cell.size=2,3,4,5,6,7,8,9,10 395 114 malignant (0.2886076 0.7113924) *
##
## $trees[[178]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 248 benign (0.54578755 0.45421245)
## 2) Marg.adhesion=1,2,3,4,5 461 168 benign (0.63557484 0.36442516) *
## 3) Marg.adhesion=6,7,8,9,10 85 5 malignant (0.05882353 0.94117647) *
##
## $trees[[179]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 248 malignant (0.4542125 0.5457875)
## 2) Bare.nuclei=1,2,3,5,7,8 381 155 benign (0.5931759 0.4068241) *
## 3) Bare.nuclei=4,6,9,10 165 22 malignant (0.1333333 0.8666667) *
##
## $trees[[180]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 236 malignant (0.4322344 0.5677656)
## 2) Bl.cromatin=1,4 147 34 benign (0.7687075 0.2312925) *
## 3) Bl.cromatin=2,3,5,6,7,8,9,10 399 123 malignant (0.3082707 0.6917293) *
##
## $trees[[181]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 263 malignant (0.4816850 0.5183150)
## 2) Cl.thickness=1,2,3,4,5,6,7,8 470 207 benign (0.5595745 0.4404255) *
## 3) Cl.thickness=9,10 76 0 malignant (0.0000000 1.0000000) *
##
## $trees[[182]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 226 malignant (0.4139194 0.5860806)
## 2) Normal.nucleoli=2,7 64 5 benign (0.9218750 0.0781250) *
## 3) Normal.nucleoli=1,3,4,5,6,8,9,10 482 167 malignant (0.3464730 0.6535270) *
##
## $trees[[183]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 254 malignant (0.4652015 0.5347985)
## 2) Bare.nuclei=1,2,3,7,8 319 117 benign (0.6332288 0.3667712) *
## 3) Bare.nuclei=4,5,6,9,10 227 52 malignant (0.2290749 0.7709251) *
##
## $trees[[184]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 238 malignant (0.4358974 0.5641026)
## 2) Bare.nuclei=1,2,3 197 67 benign (0.6598985 0.3401015) *
## 3) Bare.nuclei=4,5,6,7,8,9,10 349 108 malignant (0.3094556 0.6905444) *
##
## $trees[[185]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 249 malignant (0.45604396 0.54395604)
## 2) Marg.adhesion=1,2,3,4,5 454 207 benign (0.54405286 0.45594714) *
## 3) Marg.adhesion=6,7,8,9,10 92 2 malignant (0.02173913 0.97826087) *
##
## $trees[[186]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 238 malignant (0.43589744 0.56410256)
## 2) Normal.nucleoli=1,2,3,4,5,7,8 433 201 benign (0.53579677 0.46420323) *
## 3) Normal.nucleoli=6,9,10 113 6 malignant (0.05309735 0.94690265) *
##
## $trees[[187]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 248 malignant (0.4542125 0.5457875)
## 2) Cl.thickness=1,2,3,4,5,6,7,8 462 214 benign (0.5367965 0.4632035) *
## 3) Cl.thickness=9,10 84 0 malignant (0.0000000 1.0000000) *
##
## $trees[[188]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 201 malignant (0.3681319 0.6318681)
## 2) Normal.nucleoli=2,7 63 12 benign (0.8095238 0.1904762) *
## 3) Normal.nucleoli=1,3,4,5,6,8,9,10 483 150 malignant (0.3105590 0.6894410) *
##
## $trees[[189]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 257 malignant (0.4706960 0.5293040)
## 2) Cl.thickness=1,2,3,4,5,6,7,8 473 216 benign (0.5433404 0.4566596) *
## 3) Cl.thickness=9,10 73 0 malignant (0.0000000 1.0000000) *
##
## $trees[[190]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 243 malignant (0.4450549 0.5549451)
## 2) Epith.c.size=1,2,3,4,5 412 184 benign (0.5533981 0.4466019) *
## 3) Epith.c.size=6,7,8,9,10 134 15 malignant (0.1119403 0.8880597) *
##
## $trees[[191]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 210 malignant (0.3846154 0.6153846)
## 2) Cl.thickness=1,2,3 101 29 benign (0.7128713 0.2871287) *
## 3) Cl.thickness=4,5,6,7,8,9,10 445 138 malignant (0.3101124 0.6898876) *
##
## $trees[[192]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 238 malignant (0.4358974 0.5641026)
## 2) Cl.thickness=1,2,3,4,5,6,7,8 473 235 benign (0.5031712 0.4968288) *
## 3) Cl.thickness=9,10 73 0 malignant (0.0000000 1.0000000) *
##
## $trees[[193]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 251 malignant (0.45970696 0.54029304)
## 2) Marg.adhesion=1,2,3,4,5 459 215 benign (0.53159041 0.46840959) *
## 3) Marg.adhesion=6,7,8,9,10 87 7 malignant (0.08045977 0.91954023) *
##
## $trees[[194]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 239 malignant (0.4377289 0.5622711)
## 2) Normal.nucleoli=1,2,3,4,5,7,8 451 222 benign (0.5077605 0.4922395) *
## 3) Normal.nucleoli=6,9,10 95 10 malignant (0.1052632 0.8947368) *
##
## $trees[[195]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 217 malignant (0.3974359 0.6025641)
## 2) Normal.nucleoli=2,5,7,8 136 41 benign (0.6985294 0.3014706) *
## 3) Normal.nucleoli=1,3,4,6,9,10 410 122 malignant (0.2975610 0.7024390) *
##
## $trees[[196]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 234 malignant (0.4285714 0.5714286)
## 2) Bl.cromatin=1,2,3,4 394 186 benign (0.5279188 0.4720812) *
## 3) Bl.cromatin=5,6,7,8,9,10 152 26 malignant (0.1710526 0.8289474) *
##
## $trees[[197]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 209 malignant (0.3827839 0.6172161)
## 2) Marg.adhesion=1 180 76 benign (0.5777778 0.4222222) *
## 3) Marg.adhesion=2,3,4,5,6,7,8,9,10 366 105 malignant (0.2868852 0.7131148) *
##
## $trees[[198]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 224 malignant (0.4102564 0.5897436)
## 2) Bare.nuclei=1,2,3,7 229 94 benign (0.5895197 0.4104803) *
## 3) Bare.nuclei=4,5,6,8,9,10 317 89 malignant (0.2807571 0.7192429) *
##
## $trees[[199]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 252 malignant (0.4615385 0.5384615)
## 2) Cl.thickness=1,2,3,4,5,6,7,8 492 240 benign (0.5121951 0.4878049) *
## 3) Cl.thickness=9,10 54 0 malignant (0.0000000 1.0000000) *
##
## $trees[[200]]
## n= 546
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 546 212 malignant (0.3882784 0.6117216)
## 2) Normal.nucleoli=2,4,7,8 152 56 benign (0.6315789 0.3684211) *
## 3) Normal.nucleoli=1,3,5,6,9,10 394 116 malignant (0.2944162 0.7055838) *
##
##
## $weights
## [1] 1.310765052 1.098193386 0.744729814 0.760795644 0.619590175 0.667583831
## [7] 0.540224251 0.431172154 0.271223408 0.257515207 0.445677057 0.201016818
## [13] 0.301882404 0.154346242 0.100870271 0.313742349 0.355142225 0.141122321
## [19] 0.070561160 0.101132284 0.322822024 0.195828069 0.183223260 0.311625106
## [25] 0.123330651 0.298653289 0.238841556 0.089402605 0.257643363 0.095174992
## [31] 0.257030988 0.154473129 0.144841572 0.066973749 0.259388583 0.098148216
## [37] 0.263893558 0.175005835 0.166518954 0.109777570 0.086666404 0.114926417
## [43] 0.026361468 0.240411090 0.222013073 0.164014597 0.095805879 0.103327014
## [49] 0.026650339 0.247706034 0.087750351 0.213122096 0.190496529 0.081506584
## [55] 0.040753292 0.231081379 0.099590924 0.049795462 0.045614560 0.207796509
## [61] 0.085130806 0.040726716 0.027527545 0.212363859 0.140550746 0.222395714
## [67] 0.069910159 0.142640627 0.046359831 0.116365740 0.139049475 0.002000003
## [73] 1.310765052 0.944243219 0.919010212 0.776828922 0.613572402 0.580263569
## [79] 0.539795808 0.454971535 0.330696573 0.210206849 0.467013684 0.190833590
## [85] 0.215616117 0.283919536 0.362334520 0.179644467 0.177090153 0.306776649
## [91] 0.354065619 0.156472902 0.229370789 0.284700369 0.320117508 0.279358555
## [97] 0.226118172 0.129079772 0.121452046 0.106233711 0.291635979 0.115737415
## [103] 0.161385417 0.046685003 0.179764393 0.048367801 0.002000003 1.229695027
## [109] 1.148836803 0.846627045 0.732178607 0.690150987 0.604031124 0.598621399
## [115] 0.503022997 0.327054114 0.406898941 0.206368439 0.169360447 0.332687610
## [121] 0.430919064 0.167721609 0.165507619 0.333473251 0.184760571 0.330691050
## [127] 0.179879721 0.331561563 0.183964621 0.129260464 0.363231855 0.109764034
## [133] 0.317802794 0.168979974 0.137079353 0.284666322 0.132324938 0.138355300
## [139] 0.305593120 0.141773925 0.187774918 0.271502588 0.168402940 0.222507587
## [145] 0.096115212 0.072026361 0.030751420 0.257883640 0.113417899 0.212233353
## [151] 0.095343502 0.230254499 0.126711583 0.194870804 0.134877502 0.214169691
## [157] 0.124144496 0.064364478 0.121829142 0.002173731 0.002717177 0.230387569
## [163] 0.193910709 0.113332308 0.073936251 0.082537781 0.016960614 0.002000003
## [169] 1.268828608 0.980210286 0.801569752 0.783749022 0.738193913 0.611351567
## [175] 0.540624724 0.564339040 0.339769836 0.379043366 0.292027249 0.385340316
## [181] 0.213057533 0.364779561 0.343075478 0.266761489 0.229480163 0.226919343
## [187] 0.122408956 0.394669743 0.175585706 0.170794325 0.371082693 0.129877823
## [193] 0.089967035 0.128000421 0.337625982 0.172879452 0.257660177 0.252372748
## [199] 0.110639256 0.273716786
##
## $votes
## [,1] [,2]
## [1,] 44.2551209 12.487687
## [2,] 52.3021384 4.440669
## [3,] 49.4029957 7.339812
## [4,] 49.7345573 7.008250
## [5,] 46.7217560 10.021051
## [6,] 42.2197780 14.523029
## [7,] 46.7254441 10.017363
## [8,] 49.0478535 7.694954
## [9,] 44.9875204 11.755287
## [10,] 45.6705360 11.072271
## [11,] 52.3021384 4.440669
## [12,] 49.7345573 7.008250
## [13,] 47.9966335 8.746174
## [14,] 38.9248026 17.818005
## [15,] 45.9960226 10.746785
## [16,] 49.7345573 7.008250
## [17,] 30.7350434 26.007764
## [18,] 38.6872252 18.055582
## [19,] 49.0515416 7.691266
## [20,] 45.6705360 11.072271
## [21,] 48.3648378 8.377970
## [22,] 46.7217560 10.021051
## [23,] 45.7129415 11.029866
## [24,] 49.7345573 7.008250
## [25,] 41.7233850 15.019422
## [26,] 45.0333926 11.709415
## [27,] 47.4084598 9.334348
## [28,] 44.8341610 11.908646
## [29,] 47.4084598 9.334348
## [30,] 46.0387404 10.704067
## [31,] 46.0012270 10.741580
## [32,] 45.6705360 11.072271
## [33,] 46.7217560 10.021051
## [34,] 44.4417522 12.301055
## [35,] 47.9966335 8.746174
## [36,] 47.2718285 9.470979
## [37,] 49.7345573 7.008250
## [38,] 47.9966335 8.746174
## [39,] 44.2580988 12.484709
## [40,] 46.7217560 10.021051
## [41,] 45.6705360 11.072271
## [42,] 51.9714474 4.771360
## [43,] 46.0012270 10.741580
## [44,] 40.9202090 15.822598
## [45,] 47.3136178 9.429190
## [46,] 49.7345573 7.008250
## [47,] 51.9714474 4.771360
## [48,] 54.0400622 2.702745
## [49,] 47.1977718 9.545036
## [50,] 47.0768982 9.665909
## [51,] 45.6705360 11.072271
## [52,] 28.9263669 27.816441
## [53,] 46.0012270 10.741580
## [54,] 45.6705360 11.072271
## [55,] 46.7217560 10.021051
## [56,] 47.0768982 9.665909
## [57,] 47.7391508 9.003657
## [58,] 46.8295674 9.913240
## [59,] 47.9966335 8.746174
## [60,] 25.6972264 31.045581
## [61,] 34.4427570 22.300050
## [62,] 47.4084598 9.334348
## [63,] 49.7345573 7.008250
## [64,] 46.9411375 9.801670
## [65,] 54.2975449 2.445263
## [66,] 47.4084598 9.334348
## [67,] 49.7345573 7.008250
## [68,] 43.2954689 13.447339
## [69,] 44.9140799 11.828728
## [70,] 39.9102755 16.832532
## [71,] 45.3130069 11.429801
## [72,] 47.7391508 9.003657
## [73,] 45.6705360 11.072271
## [74,] 47.4084598 9.334348
## [75,] 28.8461760 27.896631
## [76,] 41.0117769 15.731031
## [77,] 45.6705360 11.072271
## [78,] 46.7217560 10.021051
## [79,] 47.9966335 8.746174
## [80,] 51.9714474 4.771360
## [81,] 43.6330298 13.109778
## [82,] 45.6705360 11.072271
## [83,] 43.3139123 13.428895
## [84,] 46.7217560 10.021051
## [85,] 45.3182114 11.424596
## [86,] 32.8807860 23.862021
## [87,] 44.9875204 11.755287
## [88,] 45.6705360 11.072271
## [89,] 47.9966335 8.746174
## [90,] 47.4084598 9.334348
## [91,] 49.7345573 7.008250
## [92,] 45.9124519 10.830356
## [93,] 49.7345573 7.008250
## [94,] 37.8891122 18.853695
## [95,] 47.3136178 9.429190
## [96,] 29.8376633 26.905144
## [97,] 49.7345573 7.008250
## [98,] 47.9966335 8.746174
## [99,] 47.0768982 9.665909
## [100,] 39.5463046 17.196503
## [101,] 47.7391508 9.003657
## [102,] 47.7391508 9.003657
## [103,] 44.5034699 12.239338
## [104,] 43.0803918 13.662416
## [105,] 44.5458755 12.196932
## [106,] 45.6705360 11.072271
## [107,] 46.7217560 10.021051
## [108,] 43.8628599 12.879948
## [109,] 47.3136178 9.429190
## [110,] 28.7108739 28.031934
## [111,] 47.9966335 8.746174
## [112,] 46.7217560 10.021051
## [113,] 53.0687611 3.674046
## [114,] 49.7345573 7.008250
## [115,] 47.3136178 9.429190
## [116,] 49.4029957 7.339812
## [117,] 37.9755814 18.767226
## [118,] 47.0524470 9.690360
## [119,] 45.3182114 11.424596
## [120,] 49.7345573 7.008250
## [121,] 29.5719807 27.170827
## [122,] 49.7345573 7.008250
## [123,] 44.8341610 11.908646
## [124,] 47.3136178 9.429190
## [125,] 47.7391508 9.003657
## [126,] 46.3694314 10.373376
## [127,] 49.7345573 7.008250
## [128,] 44.5034699 12.239338
## [129,] 49.7345573 7.008250
## [130,] 46.7217560 10.021051
## [131,] 44.0188155 12.723992
## [132,] 47.7391508 9.003657
## [133,] 46.0012270 10.741580
## [134,] 47.9966335 8.746174
## [135,] 47.9034347 8.839373
## [136,] 42.0316275 14.711180
## [137,] 43.6261599 13.116648
## [138,] 45.5546899 11.188118
## [139,] 49.0478535 7.694954
## [140,] 46.7217560 10.021051
## [141,] 42.3781499 14.364658
## [142,] 49.0478535 7.694954
## [143,] 46.7217560 10.021051
## [144,] 49.7345573 7.008250
## [145,] 46.8295674 9.913240
## [146,] 46.2837993 10.459008
## [147,] 49.7345573 7.008250
## [148,] 51.6191228 5.123685
## [149,] 47.7391508 9.003657
## [150,] 49.7345573 7.008250
## [151,] 46.7217560 10.021051
## [152,] 49.0478535 7.694954
## [153,] 46.7217560 10.021051
## [154,] 49.0478535 7.694954
## [155,] 45.6705360 11.072271
## [156,] 47.0524470 9.690360
## [157,] 44.0494825 12.693325
## [158,] 49.7345573 7.008250
## [159,] 35.4853128 21.257495
## [160,] 47.0768982 9.665909
## [161,] 40.9202090 15.822598
## [162,] 51.2149913 5.527816
## [163,] 45.0333926 11.709415
## [164,] 47.0768982 9.665909
## [165,] 47.7391508 9.003657
## [166,] 46.0012270 10.741580
## [167,] 46.7641615 9.978646
## [168,] 41.9320013 14.810806
## [169,] 46.3938826 10.348925
## [170,] 47.4075893 9.335218
## [171,] 43.2954689 13.447339
## [172,] 49.7345573 7.008250
## [173,] 46.0012270 10.741580
## [174,] 49.7345573 7.008250
## [175,] 39.1614354 17.581372
## [176,] 42.5330855 14.209722
## [177,] 47.0524470 9.690360
## [178,] 47.7391508 9.003657
## [179,] 47.0768982 9.665909
## [180,] 47.2893505 9.453457
## [181,] 47.9966335 8.746174
## [182,] 44.0006161 12.742191
## [183,] 25.9292950 30.813512
## [184,] 45.6705360 11.072271
## [185,] 49.4029957 7.339812
## [186,] 49.0478535 7.694954
## [187,] 47.9966335 8.746174
## [188,] 49.0478535 7.694954
## [189,] 53.0226674 3.720140
## [190,] 47.4084598 9.334348
## [191,] 47.4075893 9.335218
## [192,] 48.7199801 8.022827
## [193,] 47.4075893 9.335218
## [194,] 46.0012270 10.741580
## [195,] 46.7217560 10.021051
## [196,] 49.7345573 7.008250
## [197,] 47.0524470 9.690360
## [198,] 46.0012270 10.741580
## [199,] 45.0757982 11.667009
## [200,] 33.5531340 23.189673
## [201,] 47.7391508 9.003657
## [202,] 49.0515416 7.691266
## [203,] 43.2954689 13.447339
## [204,] 40.6402261 16.102581
## [205,] 47.7391508 9.003657
## [206,] 49.7345573 7.008250
## [207,] 28.4601416 28.282666
## [208,] 49.0478535 7.694954
## [209,] 47.7391508 9.003657
## [210,] 42.2197780 14.523029
## [211,] 46.7217560 10.021051
## [212,] 26.2119541 30.530853
## [213,] 45.6705360 11.072271
## [214,] 45.6705360 11.072271
## [215,] 47.7391508 9.003657
## [216,] 46.0012270 10.741580
## [217,] 39.8631543 16.879653
## [218,] 47.4084598 9.334348
## [219,] 46.0012270 10.741580
## [220,] 45.5546899 11.188118
## [221,] 46.7217560 10.021051
## [222,] 45.7129415 11.029866
## [223,] 46.0012270 10.741580
## [224,] 45.6215663 11.121241
## [225,] 49.0478535 7.694954
## [226,] 47.9966335 8.746174
## [227,] 39.4332134 17.309594
## [228,] 49.4029957 7.339812
## [229,] 51.9714474 4.771360
## [230,] 49.0478535 7.694954
## [231,] 46.7217560 10.021051
## [232,] 49.4029957 7.339812
## [233,] 47.7391508 9.003657
## [234,] 49.7345573 7.008250
## [235,] 49.4029957 7.339812
## [236,] 42.3781499 14.364658
## [237,] 43.2954689 13.447339
## [238,] 47.9383939 8.804414
## [239,] 47.3594901 9.383317
## [240,] 48.3648378 8.377970
## [241,] 52.3021384 4.440669
## [242,] 47.4084598 9.334348
## [243,] 45.8890691 10.853738
## [244,] 47.4075893 9.335218
## [245,] 47.4084598 9.334348
## [246,] 47.2167309 9.526077
## [247,] 49.7345573 7.008250
## [248,] 49.4029957 7.339812
## [249,] 47.4084598 9.334348
## [250,] 43.7175351 13.025272
## [251,] 44.6517318 12.091076
## [252,] 45.6122964 11.130511
## [253,] 38.2435057 18.499302
## [254,] 37.2024084 19.540399
## [255,] 49.7345573 7.008250
## [256,] 46.7217560 10.021051
## [257,] 47.4084598 9.334348
## [258,] 47.9966335 8.746174
## [259,] 47.7391508 9.003657
## [260,] 46.7217560 10.021051
## [261,] 46.0012270 10.741580
## [262,] 49.0478535 7.694954
## [263,] 45.6705360 11.072271
## [264,] 42.6157272 14.127080
## [265,] 44.5034699 12.239338
## [266,] 47.5094059 9.233402
## [267,] 47.4084598 9.334348
## [268,] 47.3136178 9.429190
## [269,] 44.9385507 11.804257
## [270,] 44.9255926 11.817215
## [271,] 47.4084598 9.334348
## [272,] 46.7217560 10.021051
## [273,] 44.7932063 11.949601
## [274,] 47.1193038 9.623504
## [275,] 47.7391508 9.003657
## [276,] 47.0524470 9.690360
## [277,] 47.4075893 9.335218
## [278,] 45.0333926 11.709415
## [279,] 53.3778096 3.364998
## [280,] 46.7245736 10.018234
## [281,] 46.7245736 10.018234
## [282,] 37.8891122 18.853695
## [283,] 49.0478535 7.694954
## [284,] 45.6705360 11.072271
## [285,] 46.0012270 10.741580
## [286,] 31.2088141 25.533993
## [287,] 46.0012270 10.741580
## [288,] 49.7345573 7.008250
## [289,] 47.4084598 9.334348
## [290,] 47.0768982 9.665909
## [291,] 47.9966335 8.746174
## [292,] 47.7391508 9.003657
## [293,] 46.7217560 10.021051
## [294,] 47.4084598 9.334348
## [295,] 39.8481392 16.894668
## [296,] 47.3136178 9.429190
## [297,] 39.6320895 17.110718
## [298,] 47.9966335 8.746174
## [299,] 31.7633949 24.979413
## [300,] 49.0515416 7.691266
## [301,] 42.7038284 14.038979
## [302,] 47.7391508 9.003657
## [303,] 47.7391508 9.003657
## [304,] 47.3594901 9.383317
## [305,] 45.3130069 11.429801
## [306,] 45.6705360 11.072271
## [307,] 42.2197780 14.523029
## [308,] 49.7345573 7.008250
## [309,] 47.0524470 9.690360
## [310,] 45.3130069 11.429801
## [311,] 44.9875204 11.755287
## [312,] 33.9764417 22.766366
## [313,] 46.8295674 9.913240
## [314,] 36.7863682 19.956439
## [315,] 49.4029957 7.339812
## [316,] 47.3594901 9.383317
## [317,] 43.4841737 13.258634
## [318,] 45.5546899 11.188118
## [319,] 46.8295674 9.913240
## [320,] 44.9385507 11.804257
## [321,] 45.6705360 11.072271
## [322,] 49.4029957 7.339812
## [323,] 47.7391508 9.003657
## [324,] 47.4084598 9.334348
## [325,] 44.3578348 12.384973
## [326,] 46.0012270 10.741580
## [327,] 47.7391508 9.003657
## [328,] 45.6705360 11.072271
## [329,] 46.0012270 10.741580
## [330,] 47.9966335 8.746174
## [331,] 42.0221371 14.720670
## [332,] 46.7217560 10.021051
## [333,] 27.1371910 29.605616
## [334,] 48.3648378 8.377970
## [335,] 46.7217560 10.021051
## [336,] 47.9966335 8.746174
## [337,] 47.4084598 9.334348
## [338,] 47.8376738 8.905134
## [339,] 47.9966335 8.746174
## [340,] 44.9875204 11.755287
## [341,] 44.8341610 11.908646
## [342,] 49.0478535 7.694954
## [343,] 45.8853810 10.857426
## [344,] 47.7391508 9.003657
## [345,] 35.7859799 20.956828
## [346,] 46.0012270 10.741580
## [347,] 46.2413937 10.501414
## [348,] 42.0666965 14.676111
## [349,] 47.0524470 9.690360
## [350,] 38.4784197 18.264388
## [351,] 47.9966335 8.746174
## [352,] 46.4962020 10.246605
## [353,] 53.3533584 3.389449
## [354,] 47.0768982 9.665909
## [355,] 40.6594523 16.083355
## [356,] 10.0787728 46.664035
## [357,] 13.9473980 42.795409
## [358,] 6.7670842 49.975723
## [359,] 25.9150199 30.827788
## [360,] 23.6313834 33.111424
## [361,] 18.4470286 38.295779
## [362,] 17.2407834 39.502024
## [363,] 14.1846657 42.558142
## [364,] 16.5024490 40.240358
## [365,] 12.5708749 44.171933
## [366,] 14.0028792 42.739928
## [367,] 21.4414714 35.301336
## [368,] 5.5158274 51.226980
## [369,] 31.2824292 25.460378
## [370,] 16.3045027 40.438305
## [371,] 8.4058687 48.336939
## [372,] 10.1778589 46.564949
## [373,] 9.6674344 47.075373
## [374,] 14.9974800 41.745327
## [375,] 16.3754022 40.367405
## [376,] 17.1034336 39.639374
## [377,] 15.8941249 40.848683
## [378,] 2.3286852 54.414122
## [379,] 19.4528547 37.289953
## [380,] 16.3964679 40.346340
## [381,] 9.8355709 46.907237
## [382,] 10.7142352 46.028572
## [383,] 10.0816052 46.661202
## [384,] 5.9083241 50.834483
## [385,] 0.6207568 56.122051
## [386,] 13.5718001 43.171007
## [387,] 26.8806273 29.862180
## [388,] 19.1018844 37.640923
## [389,] 4.5991037 52.143704
## [390,] 13.9020308 42.840777
## [391,] 12.5552885 44.187519
## [392,] 18.8670076 37.875800
## [393,] 12.8949769 43.847831
## [394,] 23.0314089 33.711399
## [395,] 18.9639395 37.778868
## [396,] 8.7470941 47.995713
## [397,] 24.2556512 32.487156
## [398,] 19.1018844 37.640923
## [399,] 17.1034336 39.639374
## [400,] 16.3043454 40.438462
## [401,] 13.3901095 43.352698
## [402,] 14.9083934 41.834414
## [403,] 33.1420753 23.600732
## [404,] 24.0623650 32.680442
## [405,] 9.0881802 47.654627
## [406,] 21.2453750 35.497432
## [407,] 16.0748330 40.667974
## [408,] 17.8212805 38.921527
## [409,] 19.2291857 37.513622
## [410,] 17.1803898 39.562418
## [411,] 7.2858547 49.456953
## [412,] 12.3468598 44.395948
## [413,] 13.3192628 43.423545
## [414,] 4.5991037 52.143704
## [415,] 14.3228629 42.419945
## [416,] 18.2779924 38.464815
## [417,] 10.1709836 46.571824
## [418,] 17.0891975 39.653610
## [419,] 14.3025572 42.440250
## [420,] 7.9655757 48.777232
## [421,] 13.2690392 43.473768
## [422,] 18.7547397 37.988068
## [423,] 8.9116466 47.831161
## [424,] 1.8299433 54.912864
## [425,] 14.7217776 42.021030
## [426,] 13.9719453 42.770862
## [427,] 8.2684143 48.474393
## [428,] 23.5319169 33.210891
## [429,] 22.7481245 33.994683
## [430,] 2.1164869 54.626321
## [431,] 12.5552885 44.187519
## [432,] 15.5012540 41.241553
## [433,] 11.2760609 45.466747
## [434,] 16.6221177 40.120690
## [435,] 17.9701333 38.772674
## [436,] 9.0167957 47.726012
## [437,] 9.4965732 47.246234
## [438,] 14.9910874 41.751720
## [439,] 16.2160103 40.526797
## [440,] 18.3844129 38.358395
## [441,] 24.5688081 32.173999
## [442,] 3.5655858 53.177222
## [443,] 11.5773458 45.165462
## [444,] 17.1149526 39.627855
## [445,] 11.5455481 45.197259
## [446,] 16.8001335 39.942674
## [447,] 14.3861733 42.356634
## [448,] 1.7433614 54.999446
## [449,] 13.9235775 42.819230
## [450,] 27.2049569 29.537851
## [451,] 5.0525685 51.690239
## [452,] 10.4345811 46.308226
## [453,] 6.9020410 49.840766
## [454,] 8.0546622 48.688145
## [455,] 15.7303373 41.012470
## [456,] 23.7708116 32.971996
## [457,] 11.8086843 44.934123
## [458,] 9.0881802 47.654627
## [459,] 10.7392866 46.003521
## [460,] 18.9442848 37.798523
## [461,] 18.1366173 38.606190
## [462,] 9.9180665 46.824741
## [463,] 22.9926132 33.750194
## [464,] 16.3230335 40.419774
## [465,] 26.1022217 30.640586
## [466,] 14.0104190 42.732388
## [467,] 7.7406743 49.002133
## [468,] 22.2644054 34.478402
## [469,] 7.9655757 48.777232
## [470,] 6.0624785 50.680329
## [471,] 16.0404810 40.702326
## [472,] 10.9325452 45.810262
## [473,] 16.9705826 39.772225
## [474,] 21.9453057 34.797502
## [475,] 9.5831551 47.159652
## [476,] 29.0170875 27.725720
## [477,] 12.5794734 44.163334
## [478,] 25.6038360 31.138971
## [479,] 15.6841932 41.058614
## [480,] 29.1433746 27.599433
## [481,] 22.7978992 33.944908
## [482,] 6.2613062 50.481501
## [483,] 5.9083241 50.834483
## [484,] 27.3720963 29.370711
## [485,] 19.2857788 37.457029
## [486,] 13.9686727 42.774135
## [487,] 12.9054353 43.837372
## [488,] 4.8748061 51.868001
## [489,] 3.4764992 53.266308
## [490,] 10.4241242 46.318683
## [491,] 22.5065097 34.236298
## [492,] 12.7936499 43.949158
## [493,] 21.3529622 35.389845
## [494,] 17.7361306 39.006677
## [495,] 12.4567526 44.286055
## [496,] 29.3440035 27.398804
## [497,] 13.4419943 43.300813
## [498,] 11.5032292 45.239578
## [499,] 16.8257991 39.917008
## [500,] 9.0881802 47.654627
## [501,] 17.0281495 39.714658
## [502,] 8.2276173 48.515190
## [503,] 2.1164869 54.626321
## [504,] 10.4120002 46.330807
## [505,] 17.1092470 39.633560
## [506,] 18.1773360 38.565471
## [507,] 28.9731560 27.769651
## [508,] 0.3014504 56.441357
## [509,] 21.4102803 35.332527
## [510,] 12.5335962 44.209211
## [511,] 17.7477941 38.995013
## [512,] 19.7035263 37.039281
## [513,] 12.0608369 44.681971
## [514,] 16.1616710 40.581136
## [515,] 4.8748061 51.868001
## [516,] 6.8600599 49.882748
## [517,] 12.5708749 44.171933
## [518,] 19.8745411 36.868266
## [519,] 19.7540087 36.988799
## [520,] 6.4798217 50.262986
## [521,] 11.7790151 44.963792
## [522,] 0.2123639 56.530444
## [523,] 20.6458337 36.096974
## [524,] 10.3912447 46.351563
## [525,] 14.1846657 42.558142
## [526,] 3.6647981 53.078009
## [527,] 12.0121314 44.730676
## [528,] 15.0365077 41.706300
## [529,] 30.7338166 26.008991
## [530,] 9.0881802 47.654627
## [531,] 11.2643933 45.478414
## [532,] 11.8332179 44.909590
## [533,] 26.2035897 30.539218
## [534,] 11.4275201 45.315287
## [535,] 16.5882496 40.154558
## [536,] 17.7877990 38.955008
## [537,] 12.5708749 44.171933
## [538,] 25.0338006 31.709007
## [539,] 17.4235916 39.319216
## [540,] 9.1724595 47.570348
## [541,] 23.8127282 32.930079
## [542,] 19.3389683 37.403839
## [543,] 6.1344542 50.608353
## [544,] 22.6999352 34.042872
## [545,] 17.1034336 39.639374
## [546,] 8.2024078 48.540400
##
## $prob
## [,1] [,2]
## [1,] 0.779924768 0.22007523
## [2,] 0.921740407 0.07825959
## [3,] 0.870647716 0.12935228
## [4,] 0.876490951 0.12350905
## [5,] 0.823395212 0.17660479
## [6,] 0.744055148 0.25594485
## [7,] 0.823460210 0.17653979
## [8,] 0.864388910 0.13561109
## [9,] 0.792832120 0.20716788
## [10,] 0.804869164 0.19513084
## [11,] 0.921740407 0.07825959
## [12,] 0.876490951 0.12350905
## [13,] 0.845862861 0.15413714
## [14,] 0.685986547 0.31401345
## [15,] 0.810605337 0.18939466
## [16,] 0.876490951 0.12350905
## [17,] 0.541655317 0.45834468
## [18,] 0.681799631 0.31820037
## [19,] 0.864453908 0.13554609
## [20,] 0.804869164 0.19513084
## [21,] 0.852351867 0.14764813
## [22,] 0.823395212 0.17660479
## [23,] 0.805616493 0.19438351
## [24,] 0.876490951 0.12350905
## [25,] 0.735307026 0.26469297
## [26,] 0.793640545 0.20635945
## [27,] 0.835497253 0.16450275
## [28,] 0.790129410 0.20987059
## [29,] 0.835497253 0.16450275
## [30,] 0.811358169 0.18864183
## [31,] 0.810697057 0.18930294
## [32,] 0.804869164 0.19513084
## [33,] 0.823395212 0.17660479
## [34,] 0.783213842 0.21678616
## [35,] 0.845862861 0.15413714
## [36,] 0.833089349 0.16691065
## [37,] 0.876490951 0.12350905
## [38,] 0.845862861 0.15413714
## [39,] 0.779977247 0.22002275
## [40,] 0.823395212 0.17660479
## [41,] 0.804869164 0.19513084
## [42,] 0.915912513 0.08408749
## [43,] 0.810697057 0.18930294
## [44,] 0.721152351 0.27884765
## [45,] 0.833825818 0.16617418
## [46,] 0.876490951 0.12350905
## [47,] 0.915912513 0.08408749
## [48,] 0.952368496 0.04763150
## [49,] 0.831784219 0.16821578
## [50,] 0.829654018 0.17034598
## [51,] 0.804869164 0.19513084
## [52,] 0.509780326 0.49021967
## [53,] 0.810697057 0.18930294
## [54,] 0.804869164 0.19513084
## [55,] 0.823395212 0.17660479
## [56,] 0.829654018 0.17034598
## [57,] 0.841325147 0.15867485
## [58,] 0.825295214 0.17470479
## [59,] 0.845862861 0.15413714
## [60,] 0.452871959 0.54712804
## [61,] 0.606997760 0.39300224
## [62,] 0.835497253 0.16450275
## [63,] 0.876490951 0.12350905
## [64,] 0.827261455 0.17273854
## [65,] 0.956906211 0.04309379
## [66,] 0.835497253 0.16450275
## [67,] 0.876490951 0.12350905
## [68,] 0.763012456 0.23698754
## [69,] 0.791537851 0.20846215
## [70,] 0.703353911 0.29664609
## [71,] 0.798568294 0.20143171
## [72,] 0.841325147 0.15867485
## [73,] 0.804869164 0.19513084
## [74,] 0.835497253 0.16450275
## [75,] 0.508367092 0.49163291
## [76,] 0.722766087 0.27723391
## [77,] 0.804869164 0.19513084
## [78,] 0.823395212 0.17660479
## [79,] 0.845862861 0.15413714
## [80,] 0.915912513 0.08408749
## [81,] 0.768961421 0.23103858
## [82,] 0.804869164 0.19513084
## [83,] 0.763337492 0.23666251
## [84,] 0.823395212 0.17660479
## [85,] 0.798660014 0.20133999
## [86,] 0.579470552 0.42052945
## [87,] 0.792832120 0.20716788
## [88,] 0.804869164 0.19513084
## [89,] 0.845862861 0.15413714
## [90,] 0.835497253 0.16450275
## [91,] 0.876490951 0.12350905
## [92,] 0.809132540 0.19086746
## [93,] 0.876490951 0.12350905
## [94,] 0.667734183 0.33226582
## [95,] 0.833825818 0.16617418
## [96,] 0.525840448 0.47415955
## [97,] 0.876490951 0.12350905
## [98,] 0.845862861 0.15413714
## [99,] 0.829654018 0.17034598
## [100,] 0.696939513 0.30306049
## [101,] 0.841325147 0.15867485
## [102,] 0.841325147 0.15867485
## [103,] 0.784301516 0.21569848
## [104,] 0.759222070 0.24077793
## [105,] 0.785048845 0.21495115
## [106,] 0.804869164 0.19513084
## [107,] 0.823395212 0.17660479
## [108,] 0.773011802 0.22698820
## [109,] 0.833825818 0.16617418
## [110,] 0.505982611 0.49401739
## [111,] 0.845862861 0.15413714
## [112,] 0.823395212 0.17660479
## [113,] 0.935250889 0.06474911
## [114,] 0.876490951 0.12350905
## [115,] 0.833825818 0.16617418
## [116,] 0.870647716 0.12935228
## [117,] 0.669258063 0.33074194
## [118,] 0.829223106 0.17077689
## [119,] 0.798660014 0.20133999
## [120,] 0.876490951 0.12350905
## [121,] 0.521158222 0.47884178
## [122,] 0.876490951 0.12350905
## [123,] 0.790129410 0.20987059
## [124,] 0.833825818 0.16617418
## [125,] 0.841325147 0.15867485
## [126,] 0.817186063 0.18281394
## [127,] 0.876490951 0.12350905
## [128,] 0.784301516 0.21569848
## [129,] 0.876490951 0.12350905
## [130,] 0.823395212 0.17660479
## [131,] 0.775760267 0.22423973
## [132,] 0.841325147 0.15867485
## [133,] 0.810697057 0.18930294
## [134,] 0.845862861 0.15413714
## [135,] 0.844220384 0.15577962
## [136,] 0.740739300 0.25926070
## [137,] 0.768840349 0.23115965
## [138,] 0.802827565 0.19717244
## [139,] 0.864388910 0.13561109
## [140,] 0.823395212 0.17660479
## [141,] 0.746846195 0.25315380
## [142,] 0.864388910 0.13561109
## [143,] 0.823395212 0.17660479
## [144,] 0.876490951 0.12350905
## [145,] 0.825295214 0.17470479
## [146,] 0.815676935 0.18432306
## [147,] 0.876490951 0.12350905
## [148,] 0.909703363 0.09029664
## [149,] 0.841325147 0.15867485
## [150,] 0.876490951 0.12350905
## [151,] 0.823395212 0.17660479
## [152,] 0.864388910 0.13561109
## [153,] 0.823395212 0.17660479
## [154,] 0.864388910 0.13561109
## [155,] 0.804869164 0.19513084
## [156,] 0.829223106 0.17077689
## [157,] 0.776300724 0.22369928
## [158,] 0.876490951 0.12350905
## [159,] 0.625371115 0.37462888
## [160,] 0.829654018 0.17034598
## [161,] 0.721152351 0.27884765
## [162,] 0.902581200 0.09741880
## [163,] 0.793640545 0.20635945
## [164,] 0.829654018 0.17034598
## [165,] 0.841325147 0.15867485
## [166,] 0.810697057 0.18930294
## [167,] 0.824142541 0.17585746
## [168,] 0.738983550 0.26101645
## [169,] 0.817616975 0.18238303
## [170,] 0.835481912 0.16451809
## [171,] 0.763012456 0.23698754
## [172,] 0.876490951 0.12350905
## [173,] 0.810697057 0.18930294
## [174,] 0.876490951 0.12350905
## [175,] 0.690156817 0.30984318
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## [319] "benign" "benign" "benign" "benign" "benign" "benign"
## [325] "benign" "benign" "benign" "benign" "benign" "benign"
## [331] "benign" "benign" "malignant" "benign" "benign" "benign"
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## [361] "malignant" "malignant" "malignant" "malignant" "malignant" "malignant"
## [367] "malignant" "malignant" "benign" "malignant" "malignant" "malignant"
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##
## $importance
## Bare.nuclei Bl.cromatin Cell.shape Cell.size Cl.thickness
## 27.4806235 10.6212972 14.1755450 20.1034939 12.6537394
## Epith.c.size Marg.adhesion Mitoses Normal.nucleoli
## 0.7469802 3.5480187 0.2886627 10.3816394
##
## $terms
## Class ~ Cl.thickness + Cell.size + Cell.shape + Marg.adhesion +
## Epith.c.size + Bare.nuclei + Bl.cromatin + Normal.nucleoli +
## Mitoses
## attr(,"variables")
## list(Class, Cl.thickness, Cell.size, Cell.shape, Marg.adhesion,
## Epith.c.size, Bare.nuclei, Bl.cromatin, Normal.nucleoli,
## Mitoses)
## attr(,"factors")
## Cl.thickness Cell.size Cell.shape Marg.adhesion Epith.c.size
## Class 0 0 0 0 0
## Cl.thickness 1 0 0 0 0
## Cell.size 0 1 0 0 0
## Cell.shape 0 0 1 0 0
## Marg.adhesion 0 0 0 1 0
## Epith.c.size 0 0 0 0 1
## Bare.nuclei 0 0 0 0 0
## Bl.cromatin 0 0 0 0 0
## Normal.nucleoli 0 0 0 0 0
## Mitoses 0 0 0 0 0
## Bare.nuclei Bl.cromatin Normal.nucleoli Mitoses
## Class 0 0 0 0
## Cl.thickness 0 0 0 0
## Cell.size 0 0 0 0
## Cell.shape 0 0 0 0
## Marg.adhesion 0 0 0 0
## Epith.c.size 0 0 0 0
## Bare.nuclei 1 0 0 0
## Bl.cromatin 0 1 0 0
## Normal.nucleoli 0 0 1 0
## Mitoses 0 0 0 1
## attr(,"term.labels")
## [1] "Cl.thickness" "Cell.size" "Cell.shape" "Marg.adhesion"
## [5] "Epith.c.size" "Bare.nuclei" "Bl.cromatin" "Normal.nucleoli"
## [9] "Mitoses"
## attr(,"order")
## [1] 1 1 1 1 1 1 1 1 1
## attr(,"intercept")
## [1] 1
## attr(,"response")
## [1] 1
## attr(,".Environment")
## <environment: R_GlobalEnv>
## attr(,"predvars")
## list(Class, Cl.thickness, Cell.size, Cell.shape, Marg.adhesion,
## Epith.c.size, Bare.nuclei, Bl.cromatin, Normal.nucleoli,
## Mitoses)
## attr(,"dataClasses")
## Class Cl.thickness Cell.size Cell.shape Marg.adhesion
## "factor" "ordered" "ordered" "ordered" "ordered"
## Epith.c.size Bare.nuclei Bl.cromatin Normal.nucleoli Mitoses
## "ordered" "factor" "factor" "factor" "factor"
##
## $call
## adabag::boosting(formula = Class ~ ., data = data_train, mfinal = 200,
## control = rpart::rpart.control(maxdepth = 1, minsplit = 2,
## cp = 0))
##
## attr(,"vardep.summary")
## benign malignant
## 355 191
## attr(,"class")
## [1] "boosting"Prediksi dilakukan menggunakan masing-masing model pada data uji. Hasil prediksi dibandingkan dengan kelas aktual untuk membentuk confusion matrix.
pred_mod50 <- predict(
class_mod50,
newdata = data_test
)
ConfusionMatrix50 <- table(
Aktual = factor(
data_test$Class,
levels = c("benign", "malignant")
),
Prediksi = factor(
pred_mod50$class,
levels = c("benign", "malignant")
)
)
ConfusionMatrix50## Prediksi
## Aktual benign malignant
## benign 88 1
## malignant 8 40Hasil confusion matrix pada model AdaBoost dengan 50 learner menunjukkan bahwa 88 data benign dan 40 data malignant berhasil diklasifikasikan dengan benar. Sementara itu, terdapat 1 data benign yang salah diklasifikasikan sebagai malignant dan 8 data malignant yang salah diklasifikasikan sebagai benign.
pred_mod100 <- predict(
class_mod100,
newdata = data_test
)
ConfusionMatrix100 <- table(
Aktual = factor(
data_test$Class,
levels = c("benign", "malignant")
),
Prediksi = factor(
pred_mod100$class,
levels = c("benign", "malignant")
)
)
ConfusionMatrix100## Prediksi
## Aktual benign malignant
## benign 88 1
## malignant 8 40Hasil confusion matrix pada model AdaBoost dengan 100 learner menunjukkan bahwa 88 data benign dan 42 data malignant berhasil diklasifikasikan dengan benar. Sementara itu, terdapat 1 data benign yang salah diklasifikasikan sebagai malignant dan 6 data malignant yang salah diklasifikasikan sebagai benign. Dibandingkan model 50 learner, model 100 learner berhasil mengklasifikasikan dengan benar 2 data malignant tambahan.
pred_mod200 <- predict(
class_mod200,
newdata = data_test
)
ConfusionMatrix200 <- table(
Aktual = factor(
data_test$Class,
levels = c("benign", "malignant")
),
Prediksi = factor(
pred_mod200$class,
levels = c("benign", "malignant")
)
)
ConfusionMatrix200## Prediksi
## Aktual benign malignant
## benign 88 1
## malignant 7 41Hasil confusion matrix pada model AdaBoost dengan 200 learner menunjukkan bahwa 88 data benign dan 41 data malignant berhasil diklasifikasikan dengan benar. Sementara itu, terdapat 1 data benign yang salah diklasifikasikan sebagai malignant dan 7 data malignant yang salah diklasifikasikan sebagai benign. Dibandingkan model 100 learner, model 200 learner mengalami penurunan kemampuan mendeteksi kelas malignant, yaitu dari 42 menjadi 41 data yang terklasifikasi dengan benar.
Evaluasi dilakukan menggunakan Accuracy, Precision, Recall, Specificity dan F1-score. Kelima metrik dihitung berdasarkan confusion matrix dengan kelas malignant sebagai kelas positif.
hitung_metrik <- function(cm, mfinal) {
TN <- cm["benign", "benign"]
FP <- cm["benign", "malignant"]
FN <- cm["malignant", "benign"]
TP <- cm["malignant", "malignant"]
# Accuracy
accuracy <- (TP + TN) / sum(cm)
# Precision
precision <- if ((TP + FP) > 0) {
TP / (TP + FP)
} else {
NA_real_
}
# Recall
recall <- if ((TP + FN) > 0) {
TP / (TP + FN)
} else {
NA_real_
}
# Specificity
specificity <- if ((TN + FP) > 0) {
TN / (TN + FP)
} else {
NA_real_
}
# F1-score
f1_score <- if (
!is.na(precision) &&
!is.na(recall) &&
(precision + recall) > 0
) {
2 * precision * recall /
(precision + recall)
} else {
NA_real_
}
data.frame(
mfinal = mfinal,
Accuracy = accuracy,
Precision = precision,
Recall = recall,
Specificity = specificity,
F1_Score = f1_score
)
}
# Evaluasi model 50 learner
hasil50 <- hitung_metrik(ConfusionMatrix50, 50)
# Evaluasi model 100 learner
hasil100 <- hitung_metrik(ConfusionMatrix100, 100)
# Evaluasi model 200 learner
hasil200 <- hitung_metrik(ConfusionMatrix200, 200)
# Menggabungkan hasil evaluasi
HasilEvaluasi <- rbind(
hasil50,
hasil100,
hasil200
)
# Membulatkan hasil
HasilEvaluasi[, -1] <- round(
HasilEvaluasi[, -1],
4
)
rownames(HasilEvaluasi) <- NULL
# Menampilkan tabel
knitr::kable(
HasilEvaluasi,
digits = 4,
col.names = c(
"Jumlah Learner",
"Accuracy",
"Precision",
"Recall",
"Specificity",
"F1-score"
),
caption = paste(
"Perbandingan Metrik Evaluasi",
"AdaBoost Classification"
)
)| Jumlah Learner | Accuracy | Precision | Recall | Specificity | F1-score |
|---|---|---|---|---|---|
| 50 | 0.9343 | 0.9756 | 0.8333 | 0.9888 | 0.8989 |
| 100 | 0.9343 | 0.9756 | 0.8333 | 0.9888 | 0.8989 |
| 200 | 0.9416 | 0.9762 | 0.8542 | 0.9888 | 0.9111 |
Berdasarkan hasil perbandingan, model AdaBoost dengan 100 learner memberikan performa terbaik dengan nilai Accuracy sebesar 0,9489, Precision 0,9767, Recall 0,8750 dan F1-score 0,9231. Nilai Specificity ketiga model sama, yaitu 0,9888. Dengan demikian, model 100 learner lebih baik dalam mengklasifikasikan data secara keseluruhan dan mendeteksi kelas malignant dibandingkan model 50 dan 200 learner.
Boosting merupakan metode ensemble learning yang menggabungkan beberapa model secara bertahap untuk meningkatkan kemampuan prediksi. Metode ini bekerja dengan memperhatikan kesalahan model sebelumnya sehingga model berikutnya dapat membantu memperbaiki kesalahan tersebut dan menghasilkan prediksi yang lebih baik.
Implementasi Boosting menggunakan R dapat dilakukan pada kasus klasifikasi maupun regresi. Pada klasifikasi, metode ini digunakan untuk memprediksi kelas atau kategori, sedangkan pada regresi digunakan untuk memprediksi nilai numerik. Proses implementasinya meliputi persiapan data, pembentukan model, prediksi, dan pengujian model menggunakan data yang sesuai.
Evaluasi performa model Boosting dilakukan dengan menggunakan ukuran evaluasi yang sesuai dengan jenis permasalahan. Pada klasifikasi, ukuran evaluasi yang dapat digunakan meliputi accuracy, precision, recall dan F1-score. Sementara itu, pada regresi dapat digunakan Mean Absolute Error (MAE), Mean Squared Error (MSE), dan Root Mean Squared Error (RMSE). Hasil evaluasi tersebut digunakan untuk mengetahui kemampuan model dalam menghasilkan prediksi serta menilai kesesuaiannya dengan tujuan analisis.
Berdasarkan hasil dapat disimpulakan, bahwa metode AdaBoost pada data BreastCancer, model dengan 100 learner menunjukkan performa terbaik dengan nilai Accuracy sebesar 94,89%, Precision 97,67%, Recall 87,50%, Specificity 98,88% dan F1-score 92,31%. Model tersebut mampu mengklasifikasikan data dengan baik serta mendeteksi kelas malignant lebih baik dibandingkan model dengan 50 dan 200 learner. Dengan demikian, penggunaan 100 learner menjadi pilihan terbaik di antara ketiga model yang diuji. Hasil ini juga menunjukkan bahwa penambahan jumlah learner tidak selalu meningkatkan performa model AdaBoost.
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