Scikit-Learn RandomizedSearchCV RidgeClassifier

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 RidgeClassifier, 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.

RidgeClassifier is a linear model that performs classification with L2 regularization. It estimates the decision boundary for binary classification tasks by minimizing the squared loss function with a penalty on the size of the coefficients.

Key hyperparameters for RidgeClassifier include the regularization strength (alpha), which controls model complexity and helps prevent overfitting; the solver (solver), which determines the optimization method used to find the model coefficients; and the tolerance for stopping criteria (tol).

from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split, RandomizedSearchCV
from sklearn.linear_model import RidgeClassifier
from scipy.stats import uniform

# Generate synthetic binary classification dataset
X, y = make_classification(n_samples=1000, n_features=20, n_informative=15, 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 = RidgeClassifier(random_state=42)

# Define hyperparameter distribution
param_dist = {
    'alpha': uniform(loc=0, scale=10),
    'solver': ['auto', 'svd', 'cholesky', 'lsqr', 'sparse_cg', 'sag', 'saga'],
    'tol': uniform(loc=0.0001, scale=0.01)
}

# 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: 0.806
Best parameters: {'alpha': 4.271077886262563, 'solver': 'lsqr', 'tol': 0.00362568856334169}
Test set accuracy: 0.815

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 RidgeClassifier model.
4. Define the hyperparameter distributions for alpha, solver, and tol with different values.
5. Perform random search using RandomizedSearchCV, specifying the RidgeClassifier model, hyperparameter distribution, 100 iterations, 5-fold cross-validation, and accuracy scoring metric.
6. Report the best cross-validation score and best set of hyperparameters found by random search.
7. 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 RidgeClassifier model.