Understanding Confusion Matrix for Model Evaluation in Machine Learning

Machine learning models are powerful tools that can make predictions, classify data, and assist in decision-making across various domains. However, it’s crucial to assess the performance of these models to ensure they are making accurate predictions. One common tool used for evaluating the performance of classification models is the confusion matrix.

What is a Confusion Matrix?

A confusion matrix is a table that is used to evaluate the performance of a classification model. It provides a detailed breakdown of the model’s predictions and their actual outcomes. In a binary classification problem, a confusion matrix typically consists of four values:

1. True Positives (TP): These are cases where the model correctly predicted the positive class. For example, in a medical diagnosis scenario, this would be cases where the model correctly identified patients with a particular disease.
2. True Negatives (TN): These are cases where the model correctly predicted the negative class. In the medical diagnosis example, it would be cases where the model correctly identified patients without the disease.
3. False Positives (FP): Also known as Type I errors, these are cases where the model incorrectly predicted the positive class…