🌿 Evaluating and Validating Machine Learning Models

Train/Test Split: The dataset is split (e.g., 80/20) into a training set for learning and a test set for evaluation.

• Cross-Validation: K-fold cross-validation splits data into K folds, training on K-1 and validating on the remaining fold repeatedly to ensure robustness.
• Metrics for Regression: Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared ($R^2$).
• Metrics for Classification: Accuracy, Precision, Recall, F1 Score, Confusion Matrix, and ROC-AUC score.