| Classification |
Accuracy |
(TP + TN) / (TP + TN + FP + FN) |
1 |
Overall performance; misleading on imbalanced datasets |
| Classification |
Balanced Accuracy |
(Sensitivity + Specificity) / 2 |
1 |
More robust for imbalanced classes |
| Classification |
Precision |
TP / (TP + FP) |
1 |
When false positives are costly (e.g., spam filtering) |
| Classification |
Recall (Sensitivity) |
TP / (TP + FN) |
1 |
When false negatives are costly (e.g., medical diagnosis) |
| Classification |
Specificity |
TN / (TN + FP) |
1 |
True negative rate; complements recall |
| Classification |
F1 Score |
2 * (Precision * Recall) / (Precision + Recall) |
1 |
Balances precision and recall; good for imbalanced datasets |
| Classification |
ROC-AUC |
Area under ROC curve |
1 |
Threshold-independent measure of classification performance |
| Classification |
Log Loss |
Penalizes confident wrong predictions |
0 |
For probabilistic classifiers |
| Classification |
Confusion Matrix |
Table showing counts of TP, FP, FN, TN |
N/A |
Detailed breakdown of errors per class |
| Regression |
MAE (Mean Absolute Error) |
Mean of absolute differences between predicted and actual values |
0 |
Simple, interpretable average error |
| Regression |
MSE (Mean Squared Error) |
Mean of squared differences between predicted and actual values |
0 |
Penalizes larger errors more than MAE |
| Regression |
RMSE (Root Mean Squared Error) |
Square root of MSE |
0 |
Same units as target; sensitive to outliers |
| Regression |
R² Score |
1 - (Sum of squared residuals / Total sum of squares) |
1 |
Proportion of variance explained |
| Regression |
Adjusted R² |
R² adjusted for number of predictors |
1 |
More accurate for comparing models with different features |