Scikit-Learn RandomizedSearchCV AdaBoostClassifier

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 an AdaBoostClassifier, commonly used for boosting the performance of binary 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.

AdaBoostClassifier is an ensemble learning method that combines multiple weak classifiers to form a strong classifier by sequentially adding models that correct errors of the previous ones. The model is trained by minimizing the exponential loss function.

Key hyperparameters for AdaBoostClassifier include the number of boosting rounds (n_estimators), which controls the number of weak classifiers to be added; the learning rate (learning_rate), which determines the contribution of each classifier; and the algorithm (algorithm), which specifies the boosting algorithm to be used (‘SAMME’ or ‘SAMME.R’).

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

# Generate synthetic binary classification dataset
X, y = make_classification(n_samples=100, n_features=20, n_informative=15, n_redundant=5, 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 = AdaBoostClassifier(random_state=42)

# Define hyperparameter distribution
param_dist = {
    'n_estimators': randint(10, 50),
    'learning_rate': uniform(0.01, 1.0),
    'algorithm': ['SAMME', 'SAMME.R']
}

# 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: 0.775
Best parameters: {'algorithm': 'SAMME', 'learning_rate': 0.4098609717152555, 'n_estimators': 37}
Test set accuracy: 0.600

The steps are as follows:

1. Generate a synthetic binary classification dataset using make_classification.
2. Split the dataset into train and test sets using train_test_split.
3. Define the AdaBoostClassifier model.
4. Define the hyperparameter distributions for n_estimators, learning_rate, and algorithm.
5. Perform random search using RandomizedSearchCV, specifying the model, hyperparameter distributions, 100 iterations, 5-fold cross-validation, and accuracy as the scoring metric.
6. Report the best cross-validation score and the 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 AdaBoostClassifier model.