Confusion Matrix

A confusion matrix is a table used to evaluate the performance of a classification model. It compares the actual target values with the predictions made by the model, providing insights into how many instances were correctly or incorrectly classified. The matrix typically includes true positives, true negatives, false positives, and false negatives, allowing for the calculation of various performance metrics such as accuracy, precision, recall, and F1 score. Common use cases for confusion matrices include assessing the effectiveness of machine learning algorithms in tasks like image recognition, spam detection, and medical diagnosis.