Scikit-Learn RandomizedSearchCV SGDClassifier

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 an SGDClassifier, a model commonly used for large-scale and sparse machine learning 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.

SGDClassifier is a linear model that uses stochastic gradient descent for optimization. It is suitable for large-scale and sparse machine learning problems, making it a popular choice for tasks like text classification and large datasets.

Key hyperparameters for SGDClassifier include the regularization term (alpha), which controls model complexity and helps prevent overfitting; the penalty type (l2, l1, or elasticnet), which determines the type of regularization applied; and the maximum iterations (max_iter), which define the number of passes over the training data.

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

# Generate synthetic binary classification dataset
X, y = make_classification(n_samples=1000, n_features=20, n_informative=10, n_redundant=10, 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 = SGDClassifier(random_state=42)

# Define hyperparameter distribution
param_dist = {
    'alpha': uniform(loc=1e-4, scale=1e-2),
    'penalty': ['l2', 'l1', 'elasticnet'],
    'max_iter': [1000, 2000, 3000]
}

# 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: 0.836
Best parameters: {'alpha': 0.008972127425763265, 'max_iter': 1000, 'penalty': 'elasticnet'}
Test set accuracy: 0.855

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 SGDClassifier model.
4. Define the hyperparameter distribution with values for alpha, penalty, and max_iter.
5. Perform random search using RandomizedSearchCV, specifying the SGDClassifier model, hyperparameter distribution, 100 iterations, 5-fold cross-validation, and accuracy scoring metric.
6. Report the best cross-validation score and the 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 SGDClassifier model.