Find your next starting point.

A guide to model-evaluation splits, including grouped and time-series data, nested model selection and permutation-based assessment.

A guide to variance filters, univariate tests, recursive elimination, model-based selection and selection inside pipelines.

A probability-calibration guide covering reliability curves and sigmoid, isotonic and temperature-scaling approaches.

A guide distinguishing outlier detection from novelty detection and comparing assumptions of several anomaly-detection methods.

A broad scoring reference that distinguishes classification, regression, ranking and clustering metrics and their scorer interfaces.

A guide to partial-dependence and individual-conditional-expectation curves, including definitions, computation and correlated-input caveats.

A model-inspection guide explaining permutation importance and its limitations when predictors are strongly correlated.

A guide to dual variables, transformations, problem arithmetic and canonical forms for convex optimization.

The bootstrap reference explains percentile, basic and bias-corrected accelerated confidence intervals, paired resampling and computation controls.

A tutorial on algorithms for graphs represented as sparse matrices, illustrated through path finding.

A reference for population-based stochastic optimization with bounds, constraints, initialization, updating and random-state controls.

A guide to constructing optimization expressions with known signs and curvature under disciplined convex programming rules.