Hyperparameter Tuning Tutorials

Step-by-step tutorials for common real-world scenarios. Each tutorial is self-contained and includes a baseline comparison, runnable code, visualizations, and practical notes.

Tutorials vs Examples

Tutorials are end-to-end walkthroughs of a complete real-world task — from raw data to a tuned, evaluated model. Examples are shorter, focused recipes that each demonstrate a single feature you can drop into your own code.

scikit-learn Estimators

Tutorial	Difficulty	What it covers
Random Forest Hyperparameter Tuning	Intermediate	7-parameter joint search, which params matter, classification and regression, baseline comparison
Gradient Boosting Hyperparameter Tuning	Intermediate	HistGradientBoosting vs classic GBM, max_leaf_nodes vs max_depth, speed comparison
Logistic Regression Hyperparameter Tuning	Beginner	C, penalty, solver compatibility, multi-penalty search with SAGA
SVM Hyperparameter Tuning (C, kernel, gamma)	Intermediate	C–gamma interaction, Pipeline with StandardScaler, RBF vs linear kernel, scaling limits

Gradient Boosting Libraries

Tutorial	Difficulty	What it covers
XGBoost Hyperparameter Tuning	Intermediate	9-parameter XGBoost search, adaptive schedules, feature importance, 3-way comparison
LightGBM Hyperparameter Tuning	Intermediate	9-parameter LightGBM search, num_leaves/max_depth interaction, parameter scatter plots
CatBoost Hyperparameter Tuning	Intermediate	7-parameter CatBoost search, bagging_temperature, border_count, GPU tip

Feature Selection

Tutorial	Difficulty	What it covers
Feature Selection with Genetic Algorithms	Advanced	3-stage workflow: select on 50 features, retune on selected subset, validate with a second estimator

Imbalanced Data

Tutorial	Difficulty	What it covers
Hyperparameter Tuning for Imbalanced Datasets	Intermediate	95/5 imbalance, class_weight as search param, balanced_accuracy scoring, confusion matrices

Outlier Detection

Tutorial	Difficulty	What it covers
Isolation Forest Hyperparameter Tuning	Advanced	Custom scorer from score_samples, 4-param search, anomaly contour plots, ROC curve

Not Sure Where to Start?

Recommended reading order

1. How Hyperparameter Optimization Works — theory and method comparison
2. When to Use Genetic Algorithm Search — decide if GASearchCV fits your problem
3. Getting Started with GASearchCV — run your first search
4. Pick the tutorial for your model above

See Also

• Examples — shorter end-to-end examples for common use cases
• Comparisons — honest benchmarks: GA vs Random vs Bayesian
• Common Hyperparameter Tuning Mistakes — avoid the most frequent pitfalls
• Choosing the Right Search Space — define good parameter bounds
• API Reference — full parameter documentation