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Assignment.Rmd
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---
title: "Assignment"
author: "Antoine Foti"
date: "April 15, 2016"
output: html_document
---
## Loading necessary libraries
```{r}
library(RWeka)
```
Loading the 'Sonar' dataset:
```{r}
data.sonar <- read.csv('sonar.all-data', header = FALSE)
glimpse(data.sonar)
View(data.sonar)
```
Column V61 (last column) is the class attribute.
## The C4.5 classifier:
```{r}
c4.5 <- J48(V61~., data=data.sonar)
summary(c4.5)
summary(c4.5)$confusionMatrix
TP <- summary(c4.5)$confusionMatrix[1,1]
TN <- summary(c4.5)$confusionMatrix[2,2]
FP <- summary(c4.5)$confusionMatrix[2,1]
FN <- summary(c4.5)$confusionMatrix[1,2]
accuracy <- (TP+TN)/(TP+TN+FP+FN)
accuracy
precision <- TP/(TP+FP)
precision
recall <- TP/(TP+FN)
recall
fscore <- 2*(precision*recall/(precision+recall))
fscore
```
Now testing the C4.5 classifier using 10-fold cross-validation:
```{r}
c4.5.10fold <- evaluate_Weka_classifier(c4.5, numFolds = 10)
c4.5.10fold
c4.5.10fold$confusionMatrix
TP <- c4.5.10fold$confusionMatrix[1,1]
TN <- c4.5.10fold$confusionMatrix[2,2]
FP <- c4.5.10fold$confusionMatrix[2,1]
FN <- c4.5.10fold$confusionMatrix[1,2]
accuracy <- (TP+TN)/(TP+TN+FP+FN)
accuracy
precision <- TP/(TP+FP)
precision
recall <- TP/(TP+FN)
recall
fscore <- 2*(precision*recall/(precision+recall))
fscore
```
##Now let's try using Random Forest algorithm:
```{r}
random_forest <- make_Weka_classifier("weka/classifiers/trees/RandomForest")
random.forest <- random_forest(V61~., data=data.sonar)
summary(random.forest)
summary(random.forest)$confusionMatrix
TP <- summary(random.forest)$confusionMatrix[1,1]
TN <- summary(random.forest)$confusionMatrix[2,2]
FP <- summary(random.forest)$confusionMatrix[2,1]
FN <- summary(random.forest)$confusionMatrix[1,2]
accuracy <- (TP+TN)/(TP+TN+FP+FN)
accuracy
precision <- TP/(TP+FP)
precision
recall <- TP/(TP+FN)
recall
fscore <- 2*(precision*recall/(precision+recall))
fscore
```
Now testing the classifier using 10-fold cross-validation:
```{r}
random.forest.10fold <- evaluate_Weka_classifier(random.forest, numFolds = 10)
random.forest.10fold
random.forest.10fold$confusionMatrix
TP <- random.forest.10fold$confusionMatrix[1,1]
TN <- random.forest.10fold$confusionMatrix[2,2]
FP <- random.forest.10fold$confusionMatrix[2,1]
FN <- random.forest.10fold$confusionMatrix[1,2]
accuracy <- (TP+TN)/(TP+TN+FP+FN)
accuracy
precision <- TP/(TP+FP)
precision
recall <- TP/(TP+FN)
recall
fscore <- 2*(precision*recall/(precision+recall))
fscore
```
## Now let's try using Support Vector Machine algorithm:
```{r}
svm <- SMO(V61~., data=data.sonar)
summary(svm)
summary(svm)$confusionMatrix
TP <- summary(svm)$confusionMatrix[1,1]
TN <- summary(svm)$confusionMatrix[2,2]
FP <- summary(svm)$confusionMatrix[2,1]
FN <- summary(svm)$confusionMatrix[1,2]
accuracy <- (TP+TN)/(TP+TN+FP+FN)
accuracy
precision <- TP/(TP+FP)
precision
recall <- TP/(TP+FN)
recall
fscore <- 2*(precision*recall/(precision+recall))
fscore
```
Now testing the classifier using 10-fold cross-validation:
```{r}
svm.10fold <- evaluate_Weka_classifier(svm, numFolds = 10)
svm.10fold
svm.10fold$confusionMatrix
TP <- svm.10fold$confusionMatrix[1,1]
TN <- svm.10fold$confusionMatrix[2,2]
FP <- svm.10fold$confusionMatrix[2,1]
FN <- svm.10fold$confusionMatrix[1,2]
accuracy <- (TP+TN)/(TP+TN+FP+FN)
accuracy
precision <- TP/(TP+FP)
precision
recall <- TP/(TP+FN)
recall
fscore <- 2*(precision*recall/(precision+recall))
fscore
```
## Now let's try using Naive Bayes algorithm:
```{r}
naive_bayes <- make_Weka_classifier("weka/classifiers/bayes/NaiveBayes")
naive.bayes <- naive_bayes(V61~., data=data.sonar)
summary(naive.bayes)
summary(naive.bayes)$confusionMatrix
TP <- summary(naive.bayes)$confusionMatrix[1,1]
TN <- summary(naive.bayes)$confusionMatrix[2,2]
FP <- summary(naive.bayes)$confusionMatrix[2,1]
FN <- summary(naive.bayes)$confusionMatrix[1,2]
accuracy <- (TP+TN)/(TP+TN+FP+FN)
accuracy
precision <- TP/(TP+FP)
precision
recall <- TP/(TP+FN)
recall
fscore <- 2*(precision*recall/(precision+recall))
fscore
```
Now testing the classifier using 10-fold cross-validation:
```{r}
naive.bayes.10fold <- evaluate_Weka_classifier(naive.bayes, numFolds = 10)
naive.bayes.10fold
naive.bayes.10fold$confusionMatrix
TP <- naive.bayes.10fold$confusionMatrix[1,1]
TN <- naive.bayes.10fold$confusionMatrix[2,2]
FP <- naive.bayes.10fold$confusionMatrix[2,1]
FN <- naive.bayes.10fold$confusionMatrix[1,2]
accuracy <- (TP+TN)/(TP+TN+FP+FN)
accuracy
precision <- TP/(TP+FP)
precision
recall <- TP/(TP+FN)
recall
fscore <- 2*(precision*recall/(precision+recall))
fscore
```
## Now let's try using the Neural Networks Multi-Layer Perceptron:
```{r}
neural_network <- make_Weka_classifier("weka/classifiers/functions/MultilayerPerceptron")
neural.network <- neural_network(V61~., data=data.sonar)
summary(neural.network)
summary(neural.network)$confusionMatrix
TP <- summary(neural.network)$confusionMatrix[1,1]
TN <- summary(neural.network)$confusionMatrix[2,2]
FP <- summary(neural.network)$confusionMatrix[2,1]
FN <- summary(neural.network)$confusionMatrix[1,2]
accuracy <- (TP+TN)/(TP+TN+FP+FN)
accuracy
precision <- TP/(TP+FP)
precision
recall <- TP/(TP+FN)
recall
fscore <- 2*(precision*recall/(precision+recall))
fscore
```
Now testing the classifier using 10-fold cross-validation:
```{r}
neural.network.10fold <- evaluate_Weka_classifier(neural.network, numFolds = 10)
neural.network.10fold
neural.network.10fold$confusionMatrix
TP <- neural.network.10fold$confusionMatrix[1,1]
TN <- neural.network.10fold$confusionMatrix[2,2]
FP <- neural.network.10fold$confusionMatrix[2,1]
FN <- neural.network.10fold$confusionMatrix[1,2]
accuracy <- (TP+TN)/(TP+TN+FP+FN)
accuracy
precision <- TP/(TP+FP)
precision
recall <- TP/(TP+FN)
recall
fscore <- 2*(precision*recall/(precision+recall))
fscore
```
## Now let's try Bagging:
```{r}
bagging <- Bagging(formula(V61~.), data=data.sonar, control=Weka_control(W="weka.classifiers.trees.J48"))
summary(bagging)
summary(bagging)$confusionMatrix
TP <- summary(bagging)$confusionMatrix[1,1]
TN <- summary(bagging)$confusionMatrix[2,2]
FP <- summary(bagging)$confusionMatrix[2,1]
FN <- summary(bagging)$confusionMatrix[1,2]
accuracy <- (TP+TN)/(TP+TN+FP+FN)
accuracy
precision <- TP/(TP+FP)
precision
recall <- TP/(TP+FN)
recall
fscore <- 2*(precision*recall/(precision+recall))
fscore
```
Now testing the classifier using 10-fold cross-validation:
```{r}
bagging.10fold <- evaluate_Weka_classifier(bagging, numFolds = 10)
bagging.10fold
bagging.10fold$confusionMatrix
TP <- bagging.10fold$confusionMatrix[1,1]
TN <- bagging.10fold$confusionMatrix[2,2]
FP <- bagging.10fold$confusionMatrix[2,1]
FN <- bagging.10fold$confusionMatrix[1,2]
accuracy <- (TP+TN)/(TP+TN+FP+FN)
accuracy
precision <- TP/(TP+FP)
precision
recall <- TP/(TP+FN)
recall
fscore <- 2*(precision*recall/(precision+recall))
fscore
```
## Now let's try Boosting:
```{r}
boosting <- AdaBoostM1(V61~., data=data.sonar)
summary(boosting)
summary(boosting)$confusionMatrix
TP <- summary(boosting)$confusionMatrix[1,1]
TN <- summary(boosting)$confusionMatrix[2,2]
FP <- summary(boosting)$confusionMatrix[2,1]
FN <- summary(boosting)$confusionMatrix[1,2]
accuracy <- (TP+TN)/(TP+TN+FP+FN)
accuracy
precision <- TP/(TP+FP)
precision
recall <- TP/(TP+FN)
recall
fscore <- 2*(precision*recall/(precision+recall))
fscore
```
Now testing the classifier using 10-fold cross-validation:
```{r}
boosting.10fold <- evaluate_Weka_classifier(boosting, numFolds = 10)
boosting.10fold
boosting.10fold$confusionMatrix
TP <- boosting.10fold$confusionMatrix[1,1]
TN <- boosting.10fold$confusionMatrix[2,2]
FP <- boosting.10fold$confusionMatrix[2,1]
FN <- boosting.10fold$confusionMatrix[1,2]
accuracy <- (TP+TN)/(TP+TN+FP+FN)
accuracy
precision <- TP/(TP+FP)
precision
recall <- TP/(TP+FN)
recall
fscore <- 2*(precision*recall/(precision+recall))
fscore
```
## Working on the other datasets:
### Hepatitis
```{r}
data.hepatitis <- read.csv("hepatitis.data", header = FALSE)
glimpse(data.hepatitis)
View(data.hepatitis)
```
### Spect
Train data:
```{r}
data.spect.train <- read.csv("SPECT.train", header = FALSE)
glimpse(data.spect.train)
View(data.spect.train)
```
Test data:
```{r}
data.spect.test <- read.csv("SPECT.test", header = FALSE)
glimpse(data.spect.test)
View(data.spect.test)
```
###Pima-indians
```{r}
data.pimaindians <- read.csv("pima-indians-diabetes.data", header = FALSE)
glimpse(data.pimaindians)
View(data.pimaindians)
```