Foundations of Supervised Learning and Evaluation

Implement a train-test-validation split of a dataset from scratch.

Implement k-fold cross-validation from scratch to use training data more effectively.

Implement and interpret a confusion matrix, precision, recall, and F1-score for classification tasks.

Implement the calculation of regression metrics including Mean Squared Error (MSE) and R-squared.

Implement the plotting of a Receiver Operating Characteristic (ROC) curve and calculate the Area Under the Curve (AUC).

Implement learning curve visualization to diagnose underfitting and overfitting.

Implement stratified sampling to handle imbalanced datasets during splitting.