What are some common evaluation metrics in ML?

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In Machine Learning, evaluation metrics help measure how well a model performs on unseen data. The choice of metric depends on the type of problem (classification, regression, ranking, etc.). Here are some common evaluation metrics:

For Classification:

1. Accuracy – Percentage of correctly predicted instances. Simple but can be misleading with imbalanced data.

2. Precision – Out of all predicted positives, how many are truly positive. Useful in cases like spam detection.

3. Recall (Sensitivity/TPR) – Out of all actual positives, how many were correctly predicted. Important in medical diagnosis.

4. F1 Score – Harmonic mean of precision and recall, balances both.

5. ROC-AUC (Area Under Curve) – Measures the ability of a model to distinguish between classes.

6. Confusion Matrix – Summarizes true positives, false positives, true negatives, and false negatives.

For Regression:

1. Mean Absolute Error (MAE) – Average of absolute differences between predictions and actual values.

2. Mean Squared Error (MSE) – Average of squared differences, penalizes larger errors more.

3. Root Mean Squared Error (RMSE) – Square root of MSE, easier to interpret in original units.

4. R² (Coefficient of Determination) – Explains how much variance in the target is captured by the model.

Other Specialized Metrics:

• Log Loss / Cross-Entropy – Used for probabilistic classification.

• Mean Absolute Percentage Error (MAPE) – Measures percentage error in regression.

• Top-K Accuracy – Useful in recommendation systems or multi-class classification.

👉 In short, metrics are like "report cards" for ML models, and the right one depends on the problem type and business goal.

Would you like me to also give a real-world analogy (like evaluating a student with marks, 

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