Scikit-Learn RandomizedSearchCV GaussianNB

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 Gaussian Naive Bayes (GaussianNB) model, 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.

Gaussian Naive Bayes is a probabilistic classifier based on applying Bayes’ theorem with Gaussian distributions for continuous features. It is often used for classification tasks due to its simplicity and effectiveness.

Key hyperparameters for GaussianNB include var_smoothing, which adds a small value to the variance to avoid zero probabilities.

from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split, RandomizedSearchCV
from sklearn.naive_bayes import GaussianNB
from scipy.stats import loguniform

# Generate synthetic classification dataset
X, y = make_classification(n_samples=1000, n_features=10, n_informative=5, 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 = GaussianNB()

# Define hyperparameter distribution
param_dist = {
    'var_smoothing': loguniform(1e-9, 1e-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: 0.875
Best parameters: {'var_smoothing': 9.91564456663839e-07}
Test set accuracy: 0.860

The steps are as follows:

1. Generate a synthetic 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 and the hyperparameter distribution for var_smoothing.
4. Perform random search using RandomizedSearchCV, specifying the GaussianNB model, hyperparameter distribution, 100 iterations, 5-fold cross-validation, and accuracy scoring metric.
5. Report the best cross-validation score and the best set of hyperparameters found by random search.
6. 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 Gaussian Naive Bayes model.