Understanding machine learning model evaluation metrics — Part 2

Navigating the Metrics Landscape: A Comprehensive Guide to Evaluating Classification Models in Machine Learning

In the previous article, we discussed four common metrics for classification problems: accuracy, precision, recall, and F1 score. You can read that article here. We saw what each metric means, how to calculate it, and how to implement it using Python, numpy and scikit-learn library. In this article, we will continue with four more metrics for classification problems: area under the ROC curve or AUC, log loss, precision at k or P@k, and average precision at k or AP@k. Let’s dive right in.

Area under the ROC curve or AUC

Area under the ROC curve or AUC is a metric that measures the performance of a binary classifier across different thresholds. ROC stands for receiver operating characteristic, which is a plot that shows the trade-off between the true positive rate (TPR) and the false positive rate (FPR) of a classifier at different thresholds. The TPR is the same as recall, which is the ratio of correctly predicted positive instances to the total number of actual positive instances. The FPR is the ratio of incorrectly predicted positive instances to the total number of actual negative instances. A perfect classifier would have a TPR of 1 and a FPR of 0, which means it can correctly identify all the positive instances without any false alarms. A…