In this chapter, we will cover the following recipes:
Preparing the training and testing datasets
Building a classification model with recursive partitioning trees
Visualizing a recursive partitioning tree
Measuring the prediction performance of a recursive partitioning tree
Pruning a recursive partitioning tree
Building a classification model with a conditional inference tree
Visualizing a conditional inference tree
Measuring the prediction performance of a conditional inference tree
Classifying data with a k-nearest neighbor classifier
Classifying data with logistic regression
Classifying data with the Naïve Bayes classifier