Chapter 10. Evaluating Classifiers, Regressors, and Clusters

In this chapter, we will cover the following recipes:

• Getting classification straight with the confusion matrix
• Computing precision, recall, and F1-score
• Examining a receiver operating characteristic and the area under a curve
• Visualizing the goodness of fit
• Computing MSE and median absolute error
• Evaluating clusters with the mean silhouette coefficient
• Comparing results with a dummy classifier
• Determining MAPE and MPE
• Comparing with a dummy regressor
• Calculating the mean absolute error and the residual sum of squares
• Examining the kappa of classification
• Taking a look at the Matthews correlation coefficient

Introduction

Evaluating classifiers, regressors, and clusters is a critical multidimensional problem involving many aspects. Purely from an engineering perspective, we worry about speed, memory, and correctness. Under some circumstances, speed is everything. If memory is scarce, of course, we have to make that our priority. The...