Confusion Matrix

A confusion matrix is a performance measurement tool for classification models, displaying the counts of true positive, true negative, false positive, and false negative predictions. It provides insight into the types of errors made by the model and is useful for calculating various performance metrics, such as accuracy, precision, recall, and F1 score. Analyzing the confusion matrix helps to identify areas for improvement in model training and evaluation.