List of Tables
Chapter 1. The data science process
Chapter 3. Exploring data
Chapter 5. Choosing and evaluating models
Table 5.1. Some common classification methods
Table 5.2. From problem to approach
Table 5.3. Ideal models to calibrate against
Table 5.4. Standard two-by-two confusion matrix
Table 5.5. Example classifier performance measures
Chapter 7. Linear and logistic regression
Chapter 8. Unsupervised methods
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