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
• Handling missing data and split and surrogate variables
• Building a classification model with a conditional inference tree
• Conditional parameters in conditional inference trees
• 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