Applying CV to Decision Trees
Explore how to apply K-Fold cross-validation to decision trees to tune the maximum depth hyperparameter. Learn to use scikit-learn's cross_val_score function to evaluate model performance across folds and select the best tree depth for classification or regression tasks.
We'll cover the following...
We'll cover the following...
Chapter Goals:
- Apply K-Fold cross-validation to a decision tree
A. Decision tree depth
We've previously discussed cross-validation for tuning hyperparameters such as the α value for regularized regression. For decision trees, we can tune the tree's maximum depth hyperparameter (max_depth) by using K-Fold cross-validation.
K-Fold cross-validation gives an accurate measurement of how good the decision tree is for the dataset. We can use K-Fold cross-validation with different ...