A Visual Guide to Tuning Decision-Tree Hyperparameters

Decision tree hyperparameters are explored to understand their impact on model performance and structure. The guide visually demonstrates how parameters like `max_depth`, `ccp_alpha`, and `min_samples_split` alter the resulting tree using scikit-learn. Using the California housing dataset, performance metrics such as MAE, RMSE, and R² are compared for different configurations. This analysis highlights the trade-off between bias and variance, showing how tuning can prevent overfitting while balancing model complexity and accuracy.