ML0014 Confusion Matrix

A confusion matrix is a table that summarizes the performance of a classification model by comparing its predicted labels against the actual labels. For binary classification, it is typically organized into a 2×2 table containing:

True Positives (TP): Cases where the model correctly predicts the positive class
False Positives (FP): Cases where the model incorrectly predicts the positive class.
False Negatives (FN): Cases where the model incorrectly predicts the negative class.
True Negatives (TN): Cases where the model correctly predicts the negative class.

It provides a detailed breakdown of the model’s predictions compared to the actual outcomes, which helps in understanding not only how many predictions were correct, but also the types of errors being made.