This article explores how different decision tree hyperparameters affect performance and visual structure, using scikit-learn's DecisionTreeRegressor and the California housing dataset. It examines the impact of max_depth, ccp_alpha, min_samples_split, min_samples_leaf, and max_leaf_nodes, and demonstrates the use of cross-validation and BayesSearchCV for optimal hyperparameter tuning.
