In a binary classification task, where there are only two different class labels, there are a number of different ways to measure classification performance. Some common metrics are as follows:
- accuracy_score: Accuracy counts the number of data points in the test set that have been predicted correctly, and returns that number as a fraction of the test set size. Sticking to the example of classifying pictures as cats or dogs, accuracy indicates the fraction of pictures that have been correctly classified as containing either a cat or a dog. This is the most basic scoring function for classifiers.
- precision_score: Precision describes the ability of a classifier not to label as cat a ...
