Hyperparameter tuning with cross-validation

Use GridSearchCV or RandomizedSearchCV to simultaneously tune max_depth, min_samples_split, and min_samples_leaf for the Decision Tree. Report the best parameter combination and compare its final holdout test metrics to the un-tuned baseline.

Start in the notebook's Decision Tree training and evaluation workflow, then review the existing un-tuned baseline metrics. Use GridSearchCV or RandomizedSearchCV to tune the three named parameters; done means reporting the best combination and comparing its holdout test metrics with the baseline.

Written by the indexing model from the issue text.