Where Does It Belong? Classification

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

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