0.13.2

Release Notes

0.13.2

Documentation release. No changes to the Python package.

New Documentation

New Guides

• How Hyperparameter Optimization Works — complete conceptual guide comparing grid search, random search, Bayesian optimization, and genetic algorithms with worked Python examples and a method-selection flowchart.
• Common Hyperparameter Tuning Mistakes — ten common pitfalls (data leakage, class imbalance, bad search spaces, missing seeds, premature stopping, and more) with diagnosis and fixes.
• Choosing the Right Search Space — when to use Integer, Continuous, Categorical; when to use log-uniform; per-estimator recommended parameter ranges.
• Feature Selection Methods Compared — side-by-side comparison of filter, embedded, and wrapper methods with guidance on when GAFeatureSelectionCV is the right choice.

New Tutorials

• Random Forest Hyperparameter Tuning — 7-parameter joint search, which parameters matter most, classification and regression variants, baseline comparison.
• Gradient Boosting Hyperparameter Tuning — HistGradientBoostingClassifier vs classic GradientBoostingClassifier, max_leaf_nodes vs max_depth, speed comparison.
• Logistic Regression Hyperparameter Tuning — solver/penalty compatibility table, multi-penalty search with SAGA, mandatory scaling in a Pipeline.
• SVM Hyperparameter Tuning (C, kernel, gamma) — C–gamma interaction visualization, mandatory Pipeline + StandardScaler, RBF vs linear kernel, O(n²) scaling note.

New Comparisons Section

• Grid Search vs Random Search vs Bayesian vs Genetic Algorithms — honest equal-budget benchmark across all four methods with code and result tables.
• Optuna vs sklearn-genetic-opt — head-to-head on tabular benchmarks using the Bayesmark experimental design; honest about where each approach wins.

New Recipes Section

A new Recipes section provides 30 copy-paste ready solutions (5–10 min each) organized into seven categories: Classification (8), Regression (5), Feature Selection (4), Pipelines (4), Scoring Metrics (5), Integrations (3), and Advanced (5).

See the full documentation for the complete recipe list.

Documentation Improvements

• SEO titles and descriptions — titles on 15+ existing pages rewritten to answer the search query directly.
• Cross-linking — "See Also" sections added to all tutorial and guide pages.
• Difficulty and reading-time metadata — all tutorial pages now show difficulty level and an estimated reading time.
• README — complete rewrite of README.rst as a high-converting GitHub landing page with value proposition, when-to-use / when-not-to-use guidance, a six-tool comparison table, condensed Quick Start, visual demo section, common use cases, and learning paths.