Evaluating Model Performance

After training, it's crucial to evaluate the model to ensure it performs well on new, unseen data. Key steps include:

Using the Testing Set: The model is tested on the separate dataset that was not used during training to assess its generalization ability.

Performance Metrics: Metrics vary based on the problem type:

For classification tasks: Accuracy, Precision, Recall, F1-score, and AUC-ROC.

For regression tasks: Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared.

Confusion Matrix: A table used to evaluate the performance of classification models by comparing predicted and actual classes.

Cross-Validation: A technique that involves dividing data into multiple subsets to train and validate the model on different portions of the data, reducing bias and variance.

Evaluating the model helps identify potential issues and areas for improvement, guiding further optimization.