In this chapter, we will cover the following recipes:
- Generating error/classification confusion matrices
- Principal Component Analysis
- Generating receiver operating characteristic charts
- Building, plotting, and evaluating with classification trees
- Using random forest models for classification
- Classifying using the support vector machine approach
- Classifying using the Naive Bayes approach
- Classifying using the KNN approach
- Using neural networks for classification
- Classifying using linear discriminant function analysis
- Classifying using logistic regression
- Text classification for sentiment analysis
