Confusion Matrices and Classification Reports: A Guide to Evaluating Machine Learning Models

Confusion matrices and classification reports are crucial tools for evaluating the performance of machine learning models. These tools provide a detailed analysis of the model’s accuracy and identify which classes the model is struggling to classify. In this article, we’ll explain what confusion matrices and classification reports are and how they can help you optimize your machine learning models.

Confusion Matrices

A confusion matrix is a table that shows how well your model is performing. It helps you evaluate the model’s predictions by breaking them down into four categories: true positives, false positives, true negatives, and false negatives. True positives are the cases where the model correctly predicted an event, false positives are the cases where the model incorrectly predicted an event, true negatives are the cases where the model correctly predicted the absence of an event, and false negatives are the cases where the model incorrectly predicted the absence of an event.

• True Positives (TP): These are the cases where the model predicted a positive outcome and it was correct.
• False Positives (FP): These are the cases where the model predicted a positive outcome, but it was incorrect.