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# Remove most objects from the working environment
rm(list = ls())
options(stringsAsFactors = F)
# Prerequisites
pkgs <- c("rpart", "rpart.plot", "party",
"randomForest", "e1071")
install.packages(pkgs, depend=TRUE)
# 17.1 Preparing the data
# code listing 17.1. Preparing the breast cancer data
loc <- "http://archive.ics.uci.edu/ml/machine-learning-databases/"
ds <- "breast-cancer-wisconsin/breast-cancer-wisconsin.data"
url <- paste(loc, ds, sep = "")
url
breast <- read.table(url, sep = ",", header = FALSE, na.strings = "?")
head(breast)
# V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11
# 1 1000025 5 1 1 1 2 1 3 1 1 2
# 2 1002945 5 4 4 5 7 10 3 2 1 2
# 3 1015425 3 1 1 1 2 2 3 1 1 2
# 4 1016277 6 8 8 1 3 4 3 7 1 2
# 5 1017023 4 1 1 3 2 1 3 1 1 2
# 6 1017122 8 10 10 8 7 10 9 7 1 4
names(breast) <- c("ID", "clumpThickness", "sizeUniformity",
"shapeUniformity", "maginalAdhesion",
"singleEpithelialCellSize", "bareNuclei",
"blandChromatin", "normalNucleoli", "mitosis", "class")
table(breast$class)
# 2 4
# 458 241
df <- breast[-1]
nrow(df)
# [1] 699
df$class <- factor(df$class, levels = c(2, 4),
labels = c("benign", "malignant"))
set.seed(1234)
train <- sample(nrow(df), 0.7*nrow(df))
df.train <- df[train,]
df.validate <- df[-train,]
table(df.train$class)
# benign malignant
# 319 170
table(df.validate$class)
# benign malignant
# 139 71
#=========================================================
# 17.2 Logistic regression
# code listing 17.2. Logistic regression with glm()
## Fits the logistic regression with df.train dataset
fit.logit <- glm(class ~ ., data = df.train, family = binomial())
##Examines the model
summary(fit.logit)
# Call:
# glm(formula = class ~ ., family = binomial(), data = df.train)
#
# Deviance Residuals:
# Min 1Q Median 3Q Max
# -2.24605 -0.08012 -0.03110 0.00266 2.11576
#
# Coefficients:
# Estimate Std. Error z value Pr(>|z|)
# (Intercept) -12.4412 2.0547 -6.055 1.4e-09 ***
# clumpThickness 0.7407 0.2262 3.275 0.00106 **
# sizeUniformity -0.0320 0.3399 -0.094 0.92500
# shapeUniformity 0.2073 0.3715 0.558 0.57680
# maginalAdhesion 0.5194 0.1708 3.041 0.00236 **
# singleEpithelialCellSize -0.3217 0.2613 -1.231 0.21831
# bareNuclei 0.5851 0.1881 3.111 0.00187 **
# blandChromatin 0.8599 0.2923 2.942 0.00326 **
# normalNucleoli 0.4036 0.1828 2.208 0.02725 *
# mitosis 0.8923 0.3552 2.512 0.01200 *
# ---
# Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#
# (Dispersion parameter for binomial family taken to be 1)
#
# Null deviance: 621.04 on 477 degrees of freedom
# Residual deviance: 52.39 on 468 degrees of freedom
# (11 observations deleted due to missingness)
# AIC: 72.39
#
# Number of Fisher Scoring iterations: 9
## Classifies new cases with df.validate dataset
prob <- predict(fit.logit, df.validate, type = "response")
logit.pred <- factor(prob > .5, levels = c(FALSE, TRUE),
labels = c("benign", "malignant"))
summary(logit.pred)
# benign malignant NA's
# 130 75 5
## Evaluates the predictive accuracy
logit.perf <- table(df.validate$class, logit.pred,
dnn = c("Actual", "Predicted"))
logit.perf
# Predicted
# Actual benign malignant
# benign 129 6
# malignant 1 69
summary(logit.perf)
# Number of cases in table: 205
# Number of factors: 2
# Test for independence of all factors:
# Chisq = 176.04, df = 1, p-value = 3.55e-40
(129+69)/(129+6+1+69)*100
# [1] 96.58537
logit.fit.reduced <- step(fit.logit)
# Start: AIC=72.39
# class ~ clumpThickness + sizeUniformity + shapeUniformity + maginalAdhesion +
# singleEpithelialCellSize + bareNuclei + blandChromatin +
# normalNucleoli + mitosis
#
# Df Deviance AIC
# - sizeUniformity 1 52.399 70.399
# - shapeUniformity 1 52.686 70.686
# - singleEpithelialCellSize 1 53.910 71.910
# <none> 52.390 72.390
# - normalNucleoli 1 57.465 75.465
# - mitosis 1 57.966 75.966
# - blandChromatin 1 62.856 80.856
# - maginalAdhesion 1 63.044 81.044
# - bareNuclei 1 64.982 82.982
# - clumpThickness 1 68.323 86.323
#
# Step: AIC=70.4
# class ~ clumpThickness + shapeUniformity + maginalAdhesion +
# singleEpithelialCellSize + bareNuclei + blandChromatin +
# normalNucleoli + mitosis
#
# Df Deviance AIC
# - shapeUniformity 1 52.876 68.876
# - singleEpithelialCellSize 1 53.918 69.918
# <none> 52.399 70.399
# - normalNucleoli 1 57.916 73.916
# - mitosis 1 58.024 74.024
# - blandChromatin 1 63.272 79.272
# - maginalAdhesion 1 63.735 79.735
# - bareNuclei 1 64.985 80.985
# - clumpThickness 1 68.728 84.728
#
# Step: AIC=68.88
# class ~ clumpThickness + maginalAdhesion + singleEpithelialCellSize +
# bareNuclei + blandChromatin + normalNucleoli + mitosis
#
# Df Deviance AIC
# - singleEpithelialCellSize 1 54.154 68.154
# <none> 52.876 68.876
# - mitosis 1 59.402 73.402
# - normalNucleoli 1 60.929 74.929
# - blandChromatin 1 64.053 78.053
# - maginalAdhesion 1 64.995 78.995
# - bareNuclei 1 75.634 89.634
# - clumpThickness 1 76.942 90.942
#
# Step: AIC=68.15
# class ~ clumpThickness + maginalAdhesion + bareNuclei + blandChromatin +
# normalNucleoli + mitosis
#
# Df Deviance AIC
# <none> 54.154 68.154
# - mitosis 1 60.177 72.177
# - normalNucleoli 1 60.937 72.937
# - blandChromatin 1 64.056 76.056
# - maginalAdhesion 1 65.022 77.022
# - bareNuclei 1 76.417 88.417
# - clumpThickness 1 77.027 89.027
#========================================================================
# 17.3.1. Classical decision trees
# code listing Creating a classical decision tree with rpart()
library(rpart)
set.seed(1234)
dtree <- rpart(class ~ ., data = df.train, method = "class",
parms = list(split="information"))
print(dtree)
# n= 489
#
# node), split, n, loss, yval, (yprob)
# * denotes terminal node
#
# 1) root 489 170 benign (0.65235174 0.34764826)
# 2) sizeUniformity< 2.5 304 8 benign (0.97368421 0.02631579)
# 4) clumpThickness< 6.5 297 3 benign (0.98989899 0.01010101) *
# 5) clumpThickness>=6.5 7 2 malignant (0.28571429 0.71428571) *
# 3) sizeUniformity>=2.5 185 23 malignant (0.12432432 0.87567568)
# 6) bareNuclei< 2.5 28 13 benign (0.53571429 0.46428571)
# 12) sizeUniformity< 3.5 16 1 benign (0.93750000 0.06250000) *
# 13) sizeUniformity>=3.5 12 0 malignant (0.00000000 1.00000000) *
# 7) bareNuclei>=2.5 157 8 malignant (0.05095541 0.94904459) *
dtree$cptable
# CP nsplit rel error xerror xstd
# 1 0.81764706 0 1.00000000 1.0000000 0.06194645
# 2 0.04117647 1 0.18235294 0.1823529 0.03169642
# 3 0.01764706 3 0.10000000 0.1588235 0.02970979
# 4 0.01000000 4 0.08235294 0.1235294 0.02637116
plotcp(dtree) # figure 17.1
dtree.pruned <- prune(dtree, cp=0.01764706)
library(rpart.plot)
prp(dtree.pruned, type = 2, extra = 104,
fallen.leaves = TRUE, main="Decision Tree") # figure 17.2
dtree.pred <- predict(dtree.pruned, df.validate, type = "class")
dtree.perf <- table(df.validate$class, dtree.pred,
dnn = c("Actual", "Predicted"))
dtree.perf
# Predicted
# Actual benign malignant
# benign 129 10
# malignant 4 67
summary(dtree.perf)
# Number of cases in table: 210
# Number of factors: 2
# Test for independence of all factors:
# Chisq = 153.78, df = 1, p-value = 2.585e-35
(129+67)/(129+10+4+67)*100
# [1] 93.33333
# 17.3.2 Conditional inference trees
library(party)
fit.ctree <- ctree(class ~ ., data = df.train)
plot(fit.ctree, main="Conditional Inference Tree")
ctree.pred <- predict(fit.ctree, df.validate, type="response")
ctree.perf <- table(df.validate$class, ctree.pred,
dnn=c("Actual", "Predicted"))
ctree.perf
# Predicted
# Actual benign malignant
# benign 131 8
# malignant 4 67
summary(ctree.perf)
# Number of cases in table: 210
# Number of factors: 2
# Test for independence of all factors:
# Chisq = 160.72, df = 1, p-value = 7.875e-37
(131+67)/(131+4+67+8)*100
# [1] 94.28571
# 17.4 Random forests
# code listing 17.5. Random forest
library(randomForest)
set.seed(1234)
fit.forest <- randomForest(class ~ ., data = df.train,
na.action = na.roughfix,
importance=TRUE)
fit.forest
# Call:
# randomForest(formula = class ~ ., data = df.train, importance = TRUE, na.action = na.roughfix)
# Type of random forest: classification
# Number of trees: 500
# No. of variables tried at each split: 3
#
# OOB estimate of error rate: 2.66%
# Confusion matrix:
# benign malignant class.error
# benign 313 6 0.01880878
# malignant 7 163 0.04117647
importance(fit.forest, type = 2)
# MeanDecreaseGini
# clumpThickness 11.382432
# sizeUniformity 63.037519
# shapeUniformity 49.027649
# maginalAdhesion 4.275345
# singleEpithelialCellSize 14.504981
# bareNuclei 42.736364
# blandChromatin 22.484755
# normalNucleoli 11.375285
# mitosis 1.755382
forest.pred <- predict(fit.forest, df.validate)
forest.perf <- table(df.validate$class, forest.pred,
dnn = c("Actual", "Predicted"))
forest.perf
# Predicted
# Actual benign malignant
# benign 128 7
# malignant 2 68
summary(forest.perf)
# Number of cases in table: 205
# Number of factors: 2
# Test for independence of all factors:
# Chisq = 168.02, df = 1, p-value = 2.004e-38
(128+68)/(128+7+2+68)*100
# [1] 95.60976
# 17.5. Support vector machines
# code listing 17.6. A support vector machine
library(e1071)
set.seed(1234)
fit.svm <- svm(class ~ ., data = df.train)
fit.svm
# Call:
# svm(formula = class ~ ., data = df.train)
#
#
# Parameters:
# SVM-Type: C-classification
# SVM-Kernel: radial
# cost: 1
#
# Number of Support Vectors: 74
svm.pred <- predict(fit.svm, na.omit(df.validate))
svm.perf <- table(na.omit(df.validate)$class,
svm.pred, dnn = c("Actual", "Predicted"))
svm.perf
# Predicted
# Actual benign malignant
# benign 126 9
# malignant 0 70
# code listing Tuning an RBF support vector machine
set.seed(1234)
tuned <- tune.svm(class ~ ., data = df.train,
gamma = 10^(-6:1),
cost = 10^(-10:10))
tuned
# Parameter tuning of ‘svm’:
#
# - sampling method: 10-fold cross validation
#
# - best parameters:
# gamma cost
# 0.01 1
#
# - best performance: 0.02740302
fit.svm <- svm(class ~ ., data = df.train, gamma = .01, cost = 1)
svm.pred <- predict(fit.svm, na.omit(df.validate))
svm.perf <- table(na.omit(df.validate)$class,
svm.pred, dnn = c("Actual", "Predicted"))
svm.perf
# Predicted
# Actual benign malignant
# benign 128 7
# malignant 0 70
summary(svm.perf)
# Number of cases in table: 205
# Number of factors: 2
# Test for independence of all factors:
# Chisq = 176.7, df = 1, p-value = 2.546e-40
(128+70)/(128+7+70)*100
# [1] 96.58537
# 17.6. Choosing a best predictive solution
# code listing 17.8. Function for assessing binary classification accuracy
performance <- function(table, n=2) {
if(!all(dim(table) == c(2,2)))
stop("Must be a 2 x 2 table")
tn = table[1,1]
fp = table[1,2]
fn = table[2,1]
tp = table[2,2]
sensitivity = tp/(tp+fn)
specificity = tn/(tn+fp)
ppp = tp/(tp+fp)
npp = tn/(tn+fn)
hitrate = (tp+tn)/(tp+tn+fp+fn)
result <- paste("Sensitivity = ", round(sensitivity, n) ,
"\nSpecificity = ", round(specificity, n),
"\nPositive Predictive Value = ", round(ppp, n),
"\nNegative Predictive Value = ", round(npp, n),
"\nAccuracy = ", round(hitrate, n), "\n", sep="")
cat(result)
}
# code listing 17.9. Performance of breast cancer data classifiers
performance(logit.perf)
# Sensitivity = 0.99
# Specificity = 0.96
# Positive Predictive Value = 0.92
# Negative Predictive Value = 0.99
# Accuracy = 0.97
performance(dtree.perf)
# Sensitivity = 0.94
# Specificity = 0.93
# Positive Predictive Value = 0.87
# Negative Predictive Value = 0.97
# Accuracy = 0.93
performance(ctree.perf)
# Sensitivity = 0.94
# Specificity = 0.94
# Positive Predictive Value = 0.89
# Negative Predictive Value = 0.97
# Accuracy = 0.94
performance(forest.perf)
# Sensitivity = 0.97
# Specificity = 0.95
# Positive Predictive Value = 0.91
# Negative Predictive Value = 0.98
# Accuracy = 0.96
performance(svm.perf)
# Sensitivity = 1
# Specificity = 0.95
# Positive Predictive Value = 0.91
# Negative Predictive Value = 1
# Accuracy = 0.97
#==============================================================
# 17.7 Using the rattle package for data mining
# install.packages("rattle"), detail in the "install_rattle.R"
# The Pima Indians Diabetes dataset is no longer available due to permission restrictions
# loc <- "http://archive.ics.uci.edu/ml/machine-learning-databases/"
# ds <- "pima-indians-diabetes/pima-indians-diabetes.data"
# url <- paste(loc, ds, sep="")
#
# diabetes <- read.table(url, sep=",", header=FALSE)
# names(diabetes) <- c("npregant", "plasma", "bp", "triceps",
# "insulin", "bmi", "pedigree", "age", "class")
# diabetes$class <- factor(diabetes$class, levels=c(0,1),
# labels=c("normal", "diabetic"))
library(rattle)
# Loading required package: tibble
# Loading required package: bitops
# Rattle: A free graphical interface for data science with R.
# Version 5.4.0 Copyright (c) 2006-2020 Togaware Pty Ltd.
# Type 'rattle()' to shake, rattle, and roll your data.
#
# Attaching package: ‘rattle’
#
# The following object is masked from ‘package:randomForest’:
#
# importance
rattle()
# Loading required package: RGtk2
# 2020-11-14 11:03:35.908 rsession[43141:820420] *** WARNING: Method userSpaceScaleFactor
# in class NSView is deprecated on 10.7 and later. It should not be used in new
# applications. Use convertRectToBacking: instead.
#
# Attaching package: ‘zoo’
# The following objects are masked from ‘package:base’:
# as.Date, as.Date.numeric
# export a Decision Tree model with pmml package.
install.packages('pmml')
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