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1. Explain the importance of model evaluation and its role in assessing Machine Learning performance.
2. Discuss the concept of training set, validation set, and test set in model evaluation.
3. Explore the significance of using a confusion matrix in evaluating classification model performance.
4. Describe the calculation and interpretation of accuracy as a basic evaluation metric.
5. Investigate the use of precision and recall in evaluating classification models.
6. Explain the concept of F1-score and its advantages over accuracy for imbalanced datasets.
7. Discuss the role of ROC curves and AUC (Area Under the Curve) in evaluating binary classifiers.
8. Describe the applications of multi-class evaluation metrics like micro-average and macro-average.
9. Investigate the use of mean squared error (MSE) in evaluating regression models.
10. Explain the concept of R-squared (R²) as a measure of regression model goodness-of-fit.
11. Discuss the significance of cross-validation in obtaining more robust model evaluation results.
12. Explore the applications of K-fold cross-validation and its variations.
13. Describe the concept of bias-variance tradeoff in the context of model evaluation.
14. Investigate the use of learning curves to visualize model performance as a function of training data size.
15. Discuss the significance of overfitting and underfitting in model evaluation.
16. Explain the role of hyperparameter tuning in improving model evaluation metrics.
17. Describe the concept of confusion matrices for multi-class classification evaluation.