Scikit-Learn GridSearchCV GaussianNB

Hyperparameter tuning is a crucial step in optimizing machine learning models for best performance. In this example, we’ll demonstrate how to use scikit-learn’s GridSearchCV to perform hyperparameter tuning for Gaussian Naive Bayes, a popular algorithm for classification tasks.

Grid search is a method for evaluating different combinations of model hyperparameters to find the best performing configuration. It exhaustively searches through a specified parameter grid, trains and evaluates the model for each combination using cross-validation, and selects the hyperparameters that yield the best performance metric.

Gaussian Naive Bayes is a probabilistic classifier based on applying Bayes’ theorem with strong independence assumptions between features. It is particularly useful for high-dimensional datasets and is computationally efficient.

The key hyperparameter for Gaussian Naive Bayes is var_smoothing, which adds a small value to the variance to account for numerical stability and avoid division by zero errors.

from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.naive_bayes import GaussianNB

# Generate synthetic binary 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 parameter grid
param_grid = {
    'var_smoothing': [1e-9, 1e-8, 1e-7, 1e-6]
}

# Perform grid search
grid_search = GridSearchCV(estimator=GaussianNB(),
                           param_grid=param_grid,
                           cv=5,
                           scoring='accuracy')
grid_search.fit(X_train, y_train)

# Report best score and parameters
print(f"Best score: {grid_search.best_score_:.3f}")
print(f"Best parameters: {grid_search.best_params_}")

# Evaluate on test set
best_model = grid_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': 1e-09}
Test set accuracy: 0.860

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 parameter grid with different values for var_smoothing.
4. Perform grid search using GridSearchCV, specifying the GaussianNB model, parameter grid, 5-fold cross-validation, and accuracy scoring metric.
5. Report the best cross-validation score and best set of hyperparameters found by grid search.
6. Evaluate the best model on the hold-out test set and report the accuracy.

By using GridSearchCV, we can efficiently explore different hyperparameter settings for Gaussian Naive Bayes and find the combination that maximizes the model’s performance, saving time and effort compared to manual tuning.