Summary
This chapter presented a number of the most common measures and techniques for evaluating the performance of machine learning classification models. Although accuracy provides a simple method for examining how often a model is correct, this can be misleading in the case of rare events because the real-life cost of such events may be inversely proportional to how frequently they appear in the data.
A number of measures based on confusion matrices better capture the balance among the costs of various types of errors. Closely examining the tradeoffs between sensitivity and specificity, or precision and recall, can be a useful tool for thinking about the implications of errors in the real world. Visualizations such as the ROC curve are also helpful to this end.
It is also worth mentioning that sometimes the best measure of a model's performance is to consider how well it meets, or doesn't meet, other objectives. For instance, you may need to explain a model's logic in simple language,...
