Scikit-Learn RandomizedSearchCV BernoulliNB

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 BernoulliNB model, commonly used for 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.

BernoulliNB is a variant of the Naive Bayes classifier designed for binary/boolean features. It is often used in text classification problems where the features are binary word occurrences.

Key hyperparameters for BernoulliNB include alpha, which is the smoothing parameter that helps prevent zero probabilities in the model, and binarize, which is used to binarize the data (values above the threshold are set to 1, those below to 0).

from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split, RandomizedSearchCV
from sklearn.naive_bayes import BernoulliNB
from scipy.stats import uniform

# Generate synthetic binary classification dataset
X, y = make_classification(n_samples=1000, n_features=10, n_informative=5, n_redundant=5, random_state=42)

# Binarize the dataset to suit BernoulliNB requirements
X = (X > 0).astype(int)

# 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 = BernoulliNB()

# Define hyperparameter distribution
param_dist = {
    'alpha': uniform(loc=0, scale=1),
    'binarize': uniform(loc=0, scale=1)
}

# Perform random search
random_search = RandomizedSearchCV(estimator=model,
                                   param_distributions=param_dist,
                                   n_iter=100,
                                   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: 0.804
Best parameters: {'alpha': 0.3745401188473625, 'binarize': 0.9507143064099162}
Test set accuracy: 0.805

The steps are as follows:

1. Generate a synthetic binary classification dataset using scikit-learn’s make_classification function.
2. Binarize the dataset to suit BernoulliNB requirements.
3. Split the dataset into train and test sets using train_test_split.
4. Define the BernoulliNB model.
5. Specify the hyperparameter distributions for alpha and binarize with common ranges.
6. Perform random search using RandomizedSearchCV, specifying the BernoulliNB model, hyperparameter distribution, 100 iterations, 5-fold cross-validation, and accuracy scoring metric.
7. Report the best cross-validation score and best set of hyperparameters found by random search.
8. 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 BernoulliNB model.