Answer / Rama Kant

The primary evaluation approaches for gauging the effectiveness of a machine learning model include Cross-Validation, K-Fold Cross-Validation, and Leave-One-Out Cross-Validation. Additionally, Split Testing (Train-Test Split) is used to evaluate the generalization performance of the model on unseen data. Other metrics like Precision, Recall, F1 Score, Area Under Curve (AUC), Confusion Matrix, and ROC Curve also play a crucial role in understanding the model's efficiency.

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