Linear Classification in R with Decision Trees
In this post you will discover 7 recipes for non-linear classification with decision trees in R.
All recipes in this post use the iris flowers dataset provided with R in the datasets package. The dataset describes the measurements if iris flowers and requires classification of each observation to one of three flower species.
Classification with Decision Trees
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Classification and Regression Trees
Classification and Regression Trees (CART) split attributes based on values that minimize a loss function, such as sum of squared errors.
The following recipe demonstrates the recursive partitioning decision tree method on the iris dataset.
| 123456789101112 | # load the packagelibrary(rpart)# load datadata(iris)# fit modelfit<-rpart(Species~.,data=iris)# summarize the fitsummary(fit)# make predictionspredictions<-predict(fit,iris[,1:4],type="class")# summarize accuracytable(predictions,iris$Species) |
Learn more about the rpart function and the rpart package.
C4.5
The C4.5 algorithm is an extension of the ID3 algorithm and constructs a decision tree to maximize information gain (difference in entropy).
The following recipe demonstrates the C4.5 (called J48 in Weka) decision tree method on the iris dataset.
C4.5 method in R R| 123456789101112 | # load the packagelibrary(RWeka)# load datadata(iris)# fit modelfit<-J48(Species~.,data=iris)# summarize the fitsummary(fit)# make predictionspredictions<-predict(fit,iris[,1:4])# summarize accuracytable(predictions,iris$Species) |
Learn more about the J48 function and the RWeka package.
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PART
PART is a rule system that creates pruned C4.5 decision trees for the data set and extracts rules and those instances that are covered by the rules are removed from the training data. The process is repeated until all instances are covered by extracted rules.
The following recipe demonstrates the PART rule system method on the iris dataset.
PART method in R R| 123456789101112 | # load the packagelibrary(RWeka)# load datadata(iris)# fit modelfit<-PART(Species~.,data=iris)# summarize the fitsummary(fit)# make predictionspredictions<-predict(fit,iris[,1:4])# summarize accuracytable(predictions,iris$Species) |
Learn more about the PART function and the RWeka package.
Bagging CART
Bootstrapped Aggregation (Bagging) is an ensemble method that creates multiple models of the same type from different sub-samples of the same dataset. The predictions from each separate model are combined together to provide a superior result. This approach has shown participially effective for high-variance methods such as decision trees.
The following recipe demonstrates bagging applied to the recursive partitioning decision tree for the iris dataset.
Bagging CART in R R| 123456789101112 | # load the packagelibrary(ipred)# load datadata(iris)# fit modelfit<-bagging(Species~.,data=iris)# summarize the fitsummary(fit)# make predictionspredictions<-predict(fit,iris[,1:4],type="class")# summarize accuracytable(predictions,iris$Species) |
Learn more about the bagging function and the ipred package.
Random Forest
Random Forest is variation on Bagging of decision trees by reducing the attributes available to making a tree at each decision point to a random sub-sample. This further increases the variance of the trees and more trees are required.
The following recipe demonstrate the random forest method applied to the iris dataset.
Random Forest in R R| 123456789101112 | # load the packagelibrary(randomForest)# load datadata(iris)# fit modelfit<-randomForest(Species~.,data=iris)# summarize the fitsummary(fit)# make predictionspredictions<-predict(fit,iris[,1:4])# summarize accuracytable(predictions,iris$Species) |
Learn more about the randomForest function and the randomForest package.
Gradient Boosted Machine
Boosting is an ensemble method developed for classification for reducing bias where models are added to learn the misclassification errors in existing models. It has been generalized and adapted in the form of Gradient Boosted Machines (GBM) for use with CART decision trees for classification and regression.
The following recipe demonstrate the Gradient Boosted Machines (GBM) method in the iris dataset.
Gradient Boosted Machines in R R| 123456789101112 | # load the packagelibrary(gbm)# load datadata(iris)# fit modelfit<-gbm(Species~.,data=iris,distribution="multinomial")# summarize the fitprint(fit)# make predictionspredictions<-predict(fit,iris)# summarize accuracytable(predictions,iris$Species) |
Learn more about the gbm function and the gbm package.
Boosted C5.0
The C5.0 method is a further extension of C4.5 and pinnacle of that line of methods. It was proprietary for a long time, although the code was released recently and is available in the C50 package.
The following recipe demonstrates the C5.0 with boosting method applied to the iris dataset.
Boosted C5.0 method in R R| 123456789101112 | # load the packagelibrary(C50)# load datadata(iris)# fit modelfit<-C5.0(Species~.,data=iris,trials=10)# summarize the fitprint(fit)# make predictionspredictions<-predict(fit,iris)# summarize accuracytable(predictions,iris$Species) |
Learn more about the C5.0 function in the C50 package.
Summary
In this post you discovered 7 recipes for non-linear classification using decision trees in R using the iris flowers dataset.
Each recipe is generic and ready for you to copy and paste and modify for your own problem.
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