Scikit-Learn GridSearchCV GaussianProcessClassifier

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 GaussianProcessClassifier, an algorithm used for probabilistic 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.

GaussianProcessClassifier is a non-parametric model that uses Gaussian processes to provide probabilistic predictions. It is particularly effective for small datasets and provides uncertainty estimates for predictions.

The key hyperparameters for GaussianProcessClassifier include the kernel, which determines the covariance function of the Gaussian process (e.g., RBF, Matern) and the optimizer, which is the method used for optimizing the log-marginal likelihood (e.g., ‘fmin_l_bfgs_b’).

from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.gaussian_process import GaussianProcessClassifier
from sklearn.gaussian_process.kernels import RBF, Matern

# 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 = {
    'kernel': [RBF(), Matern()],
    'optimizer': ['fmin_l_bfgs_b']
}

# Perform grid search
grid_search = GridSearchCV(estimator=GaussianProcessClassifier(random_state=42),
                           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: 0.929
Best parameters: {'kernel': RBF(length_scale=1), 'optimizer': 'fmin_l_bfgs_b'}
Test set accuracy: 0.915

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 parameter grid with different values for kernel and optimizer hyperparameters.
4. Perform grid search using GridSearchCV, specifying the GaussianProcessClassifier 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 easily 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 GaussianProcessClassifier.