Evaluation Metrics (Accuracy, Precision, Recall, F1)

Summary

The core classification metrics — accuracy alone is misleading on imbalanced data, so precision, recall and F1 tell you what kind of errors your model actually makes.

How to Learn This

1Compute all four metrics by hand from a confusion matrix on a small example.
2Learn when to optimize for precision (spam) vs. recall (disease detection).
3Practice reading scikit-learn's `classification_report` output fluently.

XGBoost & LightGBM

ROC-AUC

More topics in Feature Engineering & Model Evaluation

Feature Scaling & Normalization One-Hot & Label Encoding Feature Selection Handling Imbalanced Data Hyperparameter Tuning (Grid/Random Search)Ensemble Methods (Bagging, Boosting)XGBoost & LightGBM ROC-AUC Confusion Matrix Bias-Variance Tradeoff Overfitting & Regularization (L1/L2)

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