Tag: Machine Learning

Maximum likelihood connects probability models to parameter learning.

Accuracy is easy to understand, but often wrong for imbalanced, ranked, or cost-sensitive tasks.

Model selection is the disciplined process of choosing among models without fooling yourself.

How models learn noise, how validation curves reveal it, and how regularization controls it.

Why preprocessing belongs inside the validation pipeline, not before the split.

How distributions become assumptions about data, labels, errors, and model behavior.