Scikit-Learn RandomizedSearchCV DecisionTreeRegressor

Hyperparameter tuning is essential for optimizing machine learning models. In this example, we’ll demonstrate how to use scikit-learn’s RandomizedSearchCV for hyperparameter tuning of a DecisionTreeRegressor model, commonly used for regression tasks.

Random search is a method for evaluating different combinations of model hyperparameters. Unlike grid search, it samples a fixed number of hyperparameter combinations from a specified distribution, making it more efficient when searching over a large hyperparameter space.

DecisionTreeRegressor is a decision tree model used for regression tasks. It predicts continuous values by learning decision rules from input features. The model is trained by minimizing the mean squared error between the predicted and actual values.

Key hyperparameters for DecisionTreeRegressor include the maximum depth (max_depth) of the tree, which controls the tree’s complexity; the minimum number of samples required to split an internal node (min_samples_split); and the minimum number of samples required to be at a leaf node (min_samples_leaf).

from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split, RandomizedSearchCV
from sklearn.tree import DecisionTreeRegressor
from sklearn.metrics import mean_squared_error
from scipy.stats import randint

# 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 the model
model = DecisionTreeRegressor(random_state=42)

# Define hyperparameter distribution
param_dist = {
    'max_depth': randint(1, 20),
    'min_samples_split': randint(2, 20),
    'min_samples_leaf': randint(1, 20)
}

# Perform random search
random_search = RandomizedSearchCV(estimator=model,
                                   param_distributions=param_dist,
                                   n_iter=100,
                                   cv=5,
                                   scoring='neg_mean_squared_error',
                                   random_state=42)
random_search.fit(X_train, y_train)

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

# Evaluate on test set
best_model = random_search.best_estimator_
mse = mean_squared_error(y_test, best_model.predict(X_test))
print(f"Test set mean squared error: {mse:.3f}")

Running the example gives an output like:

Best score: -5797.831
Best parameters: {'max_depth': 9, 'min_samples_leaf': 3, 'min_samples_split': 17}
Test set mean squared error: 5658.973

The steps are as follows:

1. Generate a synthetic regression dataset using scikit-learn’s make_regression function.
2. Split the dataset into train and test sets using train_test_split.
3. Define the model then the hyperparameter distribution with different values for max_depth, min_samples_split, and min_samples_leaf hyperparameters.
4. Perform random search using RandomizedSearchCV, specifying the DecisionTreeRegressor model, hyperparameter distribution, 100 iterations, 5-fold cross-validation, and negative mean squared error scoring metric.
5. Report the best cross-validation score and best set of hyperparameters found by random search.
6. Evaluate the best model on the hold-out test set and report the mean squared error.

By using RandomizedSearchCV, we can efficiently explore different hyperparameter settings and find the combination that maximizes the model’s performance. This automated approach saves time and effort compared to manual hyperparameter tuning and helps ensure we select the best configuration for our DecisionTreeRegressor model.