Hyperparameter Tuning (Grid Search, Random Search) — Roadmap Step & R…
Systematically searching for the best model configuration
- Level: advanced
- Estimated time: 1 days
- Roadmap: Machine Learning
Before this step
Mini projects
- Use grid search with cross-validation to tune a random forest's hyperparameters
Interview questions
- Grid search vs random search — why can random search be more efficient in high dimensions?
Study resources
- Tuning the hyper-parameters of an estimator (Article) — The official scikit-learn documentation on GridSearchCV and RandomizedSearchCV.
- Hyperparameter Tuning Explained (Video) — A practical walkthrough of grid search and random search for tuning models.
Part of
- Model Evaluation & Validation — section
- Machine Learning roadmap — the full learning path