Confusion Matrix Calculator

A confusion matrix is a table showing predictions vs actual values for classification:

• True Positive (TP): Correctly predicted positive
• True Negative (TN): Correctly predicted negative
• False Positive (FP): Incorrectly predicted positive (Type I error)
• False Negative (FN): Incorrectly predicted negative (Type II error)

• Accuracy: (TP + TN) / Total - Overall correctness
• Precision: TP / (TP + FP) - Positive prediction accuracy
• Recall (Sensitivity): TP / (TP + FN) - True positive rate
• F1 Score: 2 × (Precision × Recall) / (Precision + Recall) - Harmonic mean
• Specificity: TN / (TN + FP) - True negative rate
• MCC: Matthews Correlation Coefficient - Balanced measure

• Evaluate classification model performance
• Identify types of errors (FP vs FN)
• Choose threshold based on business needs
• Compare multiple classifiers
• Report model performance to stakeholders