Calculate classification metrics from your confusion matrix
| Balanced Accuracy | 87.50% |
| Matthews Correlation (MCC) | 0.7509 |
| Negative Predictive Value | 85.71% |
| Prevalence | 50.00% |
| False Positive Rate | 10.00% |
Model is conservative - few false positives but misses many positives.
Model is aggressive - catches most positives but has many false alarms.
When you need balance between precision and recall, especially with imbalanced classes.
Best single metric for imbalanced datasets. Range: -1 to 1.
A confusion matrix is a table that summarizes a classification model's predictions against actual labels using four counts: True Positives (TP), True Negatives (TN), False Positives (FP), and False Negatives (FN). Precision = TP / (TP + FP) measures what fraction of positive predictions were correct. Recall = TP / (TP + FN) measures what fraction of actual positives were found. F1 Score = 2 × (Precision × Recall) / (Precision + Recall) is the harmonic mean of precision and recall, balancing both metrics. Accuracy = (TP + TN) / (TP + TN + FP + FN) measures overall correctness but can be misleading with imbalanced classes. For example, a spam detector with 95% accuracy might catch only 50% of spam if 95% of emails are not spam. Choose precision when false positives are costly (e.g., medical diagnosis) and recall when false negatives are costly (e.g., fraud detection).
Built with care by Alpiaal