Section 3 of 9
sklearn, boosting, clustering, feature engineering, model evaluation, A/B testing, interpretability, fairness.
4 items in this section.
The main clustering algorithms organised by the shape each assumes a cluster has — centroid, covariance, density, graph cut, hierarchy — where each fits, and a comparative table of the family.
One corpus of arXiv abstracts, clustered nine ways — and the finding that on real text the representation usually decides more than the algorithm.
Practical wrap-up for clustering work — cluster labelling, two-corpus comparison, validation, choosing K when it isn't known, hyperparameter tuning order, common mistakes, and reproducibility.