Machine Learning for OpenCV 4 - Second Edition

Choosing the right classification metric

We talked about several essential scoring functions in Chapter 3, First Steps in Supervised Learning. Among the most fundamental metrics for classification were the following:

• Accuracy: This 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 (sklearn.metrics.accuracy_score). This is the most basic scoring function for classifiers, and we have made extensive use of it throughout this book.
• Precision: This describes the ability of a classifier not to label a positive sample as a negative (sklearn.metrics.precision_score).
• Recall (or sensitivity): This describes the ability of a classifier to retrieve all of the ...