7. Evaluating Model Performance

Once training is complete, it is essential to evaluate the model’s performance on unseen data. Common evaluation metrics include:

• Accuracy
• Precision, Recall, and F1-score
• Mean Squared Error (MSE) and Root Mean Squared Error (RMSE)
• AUC-ROC

Cross-validation techniques, such as k-fold cross-validation, can provide a more robust estimate of model performance.