This article introduces commonly used performance metrics in deep learning and machine learning. Using appropriate performance metrics would be important to compare and identify the best machine learning model for any given problem. To understand the popular metrics — accuracy, precision, recall, f1, etc., let’s first go over the confusion matrix.

Confusion matrix

A confusion matrix is a table that is often used to describe the performance of a classification model (or “classifier”) on a set of test data. There are four entries in a confusion matrix — true positive, false positive, false negative, and true negative. True positive is…

Hyejin Kim

Machine Learning | Software Engineer

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