Utilize our free Confusion Matrix Calculator to quickly evaluate the performance of your machine learning classification models. Simply input your True Positives, True Negatives, False Positives, and False Negatives to instantly get accuracy, precision, recall, and F1 score. Perfect for data scientists and ML enthusiasts seeking to understand their model's effectiveness.
Formula:
The Confusion Matrix helps visualize the performance of a classification algorithm. Each metric provides insight into different aspects of model performance:
- True Positives (TP): Actual positive cases correctly predicted as positive.
- True Negatives (TN): Actual negative cases correctly predicted as negative.
- False Positives (FP): Actual negative cases incorrectly predicted as positive (Type I error).
- False Negatives (FN): Actual positive cases incorrectly predicted as negative (Type II error).
Based on these inputs, key metrics are calculated:
- Accuracy = (TP + TN) / (TP + TN + FP + FN)
- Precision = TP / (TP + FP)
- Recall (Sensitivity) = TP / (TP + FN)
- Specificity (True Negative Rate) = TN / (TN + FP)
- F1 Score = 2 × (Precision × Recall) / (Precision + Recall)