Advanced Model Evaluation

Evaluating a model goes far beyond accuracy. In this article, we will explore:

• Evaluation metrics for classification

• ROC curve and AUC

• Precision-Recall curve

• Cross-validation

• Handling imbalanced datasets

Import Required Libraries

Generate Imbalanced Dataset

Output

Train-Test Split

Accuracy vs. F1-Score

Output

Confusion Matrix

ROC Curve and AUC

Precision-Recall Curve

Cross-Validation

Output

Summary

• Accuracy: Misleading with imbalanced data.

• F1-Score: Best for uneven classes.

• ROC-AUC: Good for binary classification.

• Precision-Recall Curve: Best when the positive class is rare.

• Cross-validation: Helps avoid overfitting by testing across multiple splits

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