What evaluation metrics are best for classification vs regression problems?

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Evaluation metrics differ for classification and regression problems because they measure distinct outcomes:

For Classification:

These metrics assess how well the model predicts discrete labels.

1. Accuracy: Proportion of correctly predicted instances. Best for balanced datasets.

2. Precision: True Positives / (True Positives + False Positives). Useful when false positives are costly.

3. Recall (Sensitivity): True Positives / (True Positives + False Negatives). Important when missing positives is risky.

4. F1-Score: Harmonic mean of precision and recall. Ideal for imbalanced classes.

5. ROC-AUC: Measures the trade-off between true positive and false positive rates. Good for evaluating binary classifiers across thresholds.

6. Confusion Matrix: Offers detailed insight into TP, TN, FP, and FN, helping assess class-specific performance.

For Regression:

These metrics evaluate how close predictions are to actual continuous values.

1. Mean Absolute Error (MAE): Average of absolute differences between predicted and actual values. Simple and interpretable.

2. Mean Squared Error (MSE): Penalizes larger errors more than MAE, useful when large errors are undesirable.

3. Root Mean Squared Error (RMSE): Square root of MSE; maintains error units, making interpretation easier.

4. R² Score (Coefficient of Determination): Measures the proportion of variance explained by the model. Values closer to 1 indicate better performance.

5. Mean Absolute Percentage Error (MAPE): Expresses error as a percentage; helpful for comparing across datasets.

Choosing the right metric depends on the problem type, data distribution, and business priorities (e.g., cost of false positives vs. false negatives).

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