Scikit-Learn GridSearchCV DecisionTreeRegressor

Hyperparameter tuning is essential for optimizing machine learning models. In this example, we’ll demonstrate how to use scikit-learn’s GridSearchCV to perform hyperparameter tuning for DecisionTreeRegressor, a popular algorithm for regression tasks.

Grid search is a method for evaluating different combinations of model hyperparameters to find the best performing configuration. It exhaustively searches through a specified parameter grid, trains and evaluates the model for each combination using cross-validation, and selects the hyperparameters that yield the best performance metric.

Decision tree regression creates a model that predicts target values by learning decision rules inferred from features. It splits the data into subsets based on feature values to make predictions.

The key hyperparameters for DecisionTreeRegressor include max_depth, which limits the maximum depth of the tree to control model complexity; min_samples_split, the minimum number of samples required to split an internal node; and min_samples_leaf, the minimum number of samples required to be at a leaf node.

from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.tree import DecisionTreeRegressor

# Generate synthetic regression dataset
X, y = make_regression(n_samples=1000, n_features=10, noise=0.1, random_state=42)

# Split into train and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Define parameter grid
param_grid = {
    'max_depth': [None, 10, 20, 30],
    'min_samples_split': [2, 5, 10],
    'min_samples_leaf': [1, 2, 4]
}

# Perform grid search
grid_search = GridSearchCV(estimator=DecisionTreeRegressor(random_state=42),
                           param_grid=param_grid,
                           cv=5,
                           scoring='neg_mean_squared_error')
grid_search.fit(X_train, y_train)

# Report best score and parameters
print(f"Best score: {grid_search.best_score_:.3f}")
print(f"Best parameters: {grid_search.best_params_}")

# Evaluate on test set
best_model = grid_search.best_estimator_
test_score = best_model.score(X_test, y_test)
print(f"Test set R^2: {test_score:.3f}")

Running the example gives an output like:

Best score: -5964.229
Best parameters: {'max_depth': None, 'min_samples_leaf': 4, 'min_samples_split': 10}
Test set R^2: 0.662

The steps are as follows:

1. Generate a synthetic regression dataset using make_regression.
2. Split the dataset into train and test sets using train_test_split.
3. Define the parameter grid with values for max_depth, min_samples_split, and min_samples_leaf hyperparameters.
4. Perform grid search using GridSearchCV, specifying the DecisionTreeRegressor model, parameter grid, 5-fold cross-validation, and mean squared error as the scoring metric.
5. Report the best cross-validation score and best set of hyperparameters found by grid search.
6. Evaluate the best model on the hold-out test set and report the R^2 score.

By using GridSearchCV, we can systematically explore different hyperparameter settings and identify the configuration that maximizes the model’s performance, making the tuning process efficient and effective for the DecisionTreeRegressor.