A Visual Guide to Tuning Random Forest Hyperparameters

An exploration of tuning hyperparameters for Random Forest models is presented using `sklearn.ensemble.RandomForestRegressor` on the California housing dataset. The guide visually demonstrates how individual trees contribute to a forest's overall prediction by analyzing their separate predictions, errors, and feature importances. It systematically tests the impact of key hyperparameters such as `n_estimators`, `max_depth`, and `ccp_alpha` on performance metrics and computational cost. The analysis also covers the benefits of parallelization with `n_jobs` and employs Bayesian search to find an optimal set of parameters for the model.