Scikit-Learn RandomizedSearchCV RandomForestRegressor

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 RandomForestRegressor, 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.

RandomForestRegressor is an ensemble learning method for regression that constructs multiple decision trees and merges them to obtain more accurate and stable predictions. The model improves performance by averaging the predictions of several trees to reduce overfitting and increase generalization.

Key hyperparameters for RandomForestRegressor include the number of trees in the forest (n_estimators), the maximum depth of each tree (max_depth), the minimum number of samples required to split an internal node (min_samples_split), the minimum number of samples required to be at a leaf node (min_samples_leaf), and whether bootstrap samples are used when building trees (bootstrap).

from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split, RandomizedSearchCV
from sklearn.ensemble import RandomForestRegressor
from scipy.stats import randint, uniform

# Generate synthetic regression dataset
X, y = make_regression(n_samples=100, 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 = RandomForestRegressor(random_state=42)

# Define hyperparameter distribution
param_dist = {
    'n_estimators': randint(10, 50),
    'max_depth': randint(1, 20),
    'min_samples_split': uniform(0.01, 0.1),
    'min_samples_leaf': uniform(0.01, 0.1),
    'bootstrap': [True, False]
}

# Perform random search
random_search = RandomizedSearchCV(estimator=model,
                                   param_distributions=param_dist,
                                   n_iter=50,
                                   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_
test_score = best_model.score(X_test, y_test)
print(f"Test set R^2 score: {test_score:.3f}")

Running the example gives an output like:

Best score: -12297.719
Best parameters: {'bootstrap': True, 'max_depth': 18, 'min_samples_leaf': 0.026080805141749867, 'min_samples_split': 0.06487337893665861, 'n_estimators': 44}
Test set R^2 score: 0.628

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 RandomForestRegressor model.
4. Define the hyperparameter distribution with different values for n_estimators, max_depth, min_samples_split, min_samples_leaf, and bootstrap hyperparameters.
5. Perform random search using RandomizedSearchCV, specifying the RandomForestRegressor model, hyperparameter distribution, 100 iterations, 5-fold cross-validation, and negative mean squared error scoring metric.
6. Report the best cross-validation score and best set of hyperparameters found by random search.
7. Evaluate the best model on the hold-out test set and report the R^2 score.

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 RandomForestRegressor model.