Confusion Matrix Calculator

A confusion matrix is a table that summarizes the performance of a classification model by showing True Positives (TP), True Negatives (TN), False Positives (FP), and False Negatives (FN). It provides a complete picture of model performance beyond simple accuracy.

Precision = TP/(TP+FP) — of all positive predictions, how many were actually positive. Recall = TP/(TP+FN) — of all actual positives, how many did the model find. High precision means fewer false alarms; high recall means fewer missed positives. The right balance depends on your use case.

Use F1 when your dataset is imbalanced (e.g., 95% negative class). Accuracy on an imbalanced dataset can be misleading — a model predicting all negatives gets 95% accuracy but 0% recall. F1 balances precision and recall and is more meaningful for imbalanced classification problems.

MCC is a balanced metric that considers all four confusion matrix values. It ranges from -1 (perfect inverse prediction) to +1 (perfect prediction), with 0 being random. MCC is considered one of the best single-value metrics for binary classification, especially for imbalanced datasets.

Cohen's Kappa measures agreement between predicted and actual labels while accounting for chance agreement. Values: <0 (worse than chance), 0-0.20 (slight), 0.21-0.40 (fair), 0.41-0.60 (moderate), 0.61-0.80 (substantial), 0.81-1.0 (near perfect). It's useful when comparing models on imbalanced datasets.