model-evaluation

Why 99% accuracy can be useless: how the confusion matrix, precision, recall, F1, and PR curves behave on imbalanced data, with a fully worked example.

Overfitting and underfitting in ML explained simply: bias-variance tradeoff, how to detect, regularization, cross-validation, and practical fixes for both.