Python Data Analysis Cookbook

Getting classification straight with the confusion matrix

Accuracy is a metric that measures how well a model has performed in a given context. Accuracy is the default evaluation metric of scikit-learn classifiers. Unfortunately, accuracy is one-dimensional, and it doesn't help when the classes are unbalanced. The rain data we examined in Chapter 9, Ensemble Learning and Dimensionality Reduction, is pretty balanced. The number of rainy days is almost equal to the number of days on which it doesn't rain. In the case of e-mail spam classification, at least for me, the balance is shifted toward spam.

A confusion matrix is a table that is usually used to summarize the results of classification. The two dimensions of the table are the predicted class ...