A tabular layout that visualizes the performance of a classification model. It compares predicted values against actual values, breaking results down into four quadrants: True Positives (TP), True Negatives (TN), False Positives (FP, Type I Error), and False Negatives (FN, Type II Error).
A foundational concept in statistical classification and machine learning evaluation.
The starting point for calculating all other critical metrics like Precision, Recall, and F1 Score.