- Splitting datasets for training, validation, and testing.
- Evaluation metrics for classification, regression, and clustering tasks.
- Techniques for model evaluation: accuracy, precision, recall, F1-score, ROC curves, etc.
- Understanding confusion matrices and interpreting model predictions.
- Model performance comparison and statistical significance.