Model Evaluation Challenges
Practice evaluating machine learning models using various metrics and validation techniques.
Intermediate
Cross-Validation Implementation
Implement k-fold cross-validation from scratch and analyze its impact on model evaluation.
Cross-Validation
Model Selection
Python
Beginner
Classification Metrics
Implement and compare different classification metrics including accuracy, precision, recall, F1-score, and ROC-AUC.
Classification Metrics
Confusion Matrix
ROC Curve
Beginner
Regression Metrics
Implement and compare different regression metrics including MSE, MAE, RMSE, and R-squared.
Regression Metrics
Error Analysis
Residuals