Tag: scikit-learn

How a compact Python ML cheatsheet becomes useful when synthetic demos, metrics, pipelines, and version drift are tied to the model-review decisions they can actually defend.

A production-friendly pattern for pairing scikit-learn preprocessing graphs with PyTorch models so training and inference use the same feature contract.

Why tabular models drift between notebooks and production when preprocessing, sample metadata, hyperparameter search, and persistence are not treated as one scikit-learn pipeline contract.

A long-form feature-screening workflow that uses correlation for quick linear checks, then adds redundancy clustering, mutual information, chi-squared tests, L1 models, tree importances, permutation importance, and domain review.

A production-friendly scikit-learn pattern for mixed tabular data, class imbalance, calibrated probabilities, threshold selection, and model persistence.