Πώς να υπολογίσετε το Confusion Matrix
Τι είναι το Confusion Matrix;
Creates confusion matrix showing actual vs. predicted classifications. Basis for evaluation metrics.
Τύπος
Accuracy = (TP+TN) / total
- TP
- TN/(TN+FP) — TN/(TN+FP)
- TN
- TN value — Variable used in the calculation
Οδηγός βήμα προς βήμα
- 14 cells: TP (correct positive), FP (false positive), FN (false negative), TN (correct negative)
- 2Accuracy = (TP+TN) / total
- 3Sensitivity/Recall = TP/(TP+FN), Specificity = TN/(TN+FP)
- 4Precision = TP/(TP+FP)
Worked Examples
Εισαγωγή
TP/FP/TN/FN
Αποτέλεσμα
Metrics calc
Common Mistakes to Avoid
- ✕Using accuracy for imbalanced data (wrong)
- ✕Confusing sensitivity and specificity
- ✕Not balancing precision/recall tradeoff
Frequently Asked Questions
When use different metrics?
Accuracy: balanced classes; precision: minimize false positives; recall: minimize false negatives.
What about imbalanced classes?
Accuracy misleading; use precision, recall, F1-score, or AUC instead.
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