A Confusion Matrix is a table used to evaluate the performance of a classification model. It summarizes the results of predictions by comparing the actual labels with the predicted labels. The matrix typically includes four key values: True Positives (TP), True Negatives (TN), False Positives (FP), and False Negatives (FN). These values help in calculating various performance metrics like accuracy, precision, recall, and F1 score. By analyzing the Confusion Matrix, data scientists can identify how well their model is performing and where it may be making errors. This insight is crucial for improving the model and ensuring it makes accurate predictions in real-world applications, such as in machine learning and artificial intelligence.