Scikit-Learn RandomizedSearchCV GaussianProcessClassifier

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 GaussianProcessClassifier, a model 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 Process Classifier is a probabilistic, non-parametric model used for classification tasks. It makes predictions based on the posterior probability of the classes.

Key hyperparameters for Gaussian Process Classifier include the kernel function (e.g., RBF, Matern), the kernel’s length scale (length_scale), and the optimizer used for fitting (optimizer).

from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split, RandomizedSearchCV
from sklearn.gaussian_process import GaussianProcessClassifier
from sklearn.gaussian_process.kernels import RBF, Matern
from scipy.stats import uniform

# Generate synthetic binary classification dataset
X, y = make_classification(n_samples=100, 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 = GaussianProcessClassifier(random_state=42)

# Define hyperparameter distribution
param_dist = {
    'kernel': [1.0 * RBF(length_scale=1.0), 1.0 * Matern(length_scale=1.0, nu=1.5)],
    'optimizer': ['fmin_l_bfgs_b'],
    'n_restarts_optimizer': [0, 1, 2],
}

# 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: 0.912
Best parameters: {'optimizer': 'fmin_l_bfgs_b', 'n_restarts_optimizer': 0, 'kernel': 1**2 * RBF(length_scale=1)}
Test set accuracy: 0.900

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 model then the hyperparameter distribution with different values for kernel, optimizer, and n_restarts_optimizer.
4. Perform random search using RandomizedSearchCV, specifying the GaussianProcessClassifier model, hyperparameter distribution, 100 iterations, 5-fold cross-validation, and accuracy scoring metric.
5. Report the best cross-validation score and 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 Process Classifier.