Scikit-Learn RandomizedSearchCV GradientBoostingClassifier

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 Gradient Boosting Classifier, commonly used for classification 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.

Gradient Boosting Classifier is an ensemble learning technique that combines weak learners to form a strong predictive model. It builds models sequentially, each new model correcting errors made by the previous ones.

Key hyperparameters for Gradient Boosting Classifier include:

• n_estimators: Number of boosting stages to be run.
• learning_rate: Shrinks the contribution of each tree.
• max_depth: Maximum depth of the individual trees.
• min_samples_split: Minimum number of samples required to split an internal node.

from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split, RandomizedSearchCV
from sklearn.ensemble import GradientBoostingClassifier
from scipy.stats import randint, uniform

# Generate synthetic binary classification dataset
X, y = make_classification(n_samples=100, n_features=20, n_informative=10, n_redundant=10, 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 = GradientBoostingClassifier(random_state=42)

# Define hyperparameter distribution
param_dist = {
    'n_estimators': randint(10, 50),
    'learning_rate': uniform(0.01, 0.3),
    'max_depth': randint(3, 10),
    'min_samples_split': randint(2, 20)
}

# Perform random search
random_search = RandomizedSearchCV(estimator=model,
                                   param_distributions=param_dist,
                                   n_iter=50,
                                   cv=5,
                                   scoring='accuracy',
                                   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_
accuracy = best_model.score(X_test, y_test)
print(f"Test set accuracy: {accuracy:.3f}")

Running the example gives an output like:

Best score: 0.850
Best parameters: {'learning_rate': 0.2597327922401265, 'max_depth': 8, 'min_samples_split': 3, 'n_estimators': 30}
Test set accuracy: 0.700

The steps are as follows:

1. Generate a synthetic binary classification dataset using scikit-learn’s make_classification function.
2. Split the dataset into train and test sets using train_test_split.
3. Define the model by initializing the GradientBoostingClassifier.
4. Define the hyperparameter distribution with different values for n_estimators, learning_rate, max_depth, and min_samples_split.
5. Perform random search using RandomizedSearchCV, specifying the GradientBoostingClassifier, hyperparameter distribution, 100 iterations, 5-fold cross-validation, and accuracy 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 accuracy.

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 Gradient Boosting Classifier.