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
