Python Data Analysis Cookbook

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 ...