Scikit-Learn GridSearchCV SGDClassifier

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 SGDClassifier, a versatile 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.

SGDClassifier implements stochastic gradient descent for classification tasks, making it suitable for large-scale datasets. It supports various loss functions and penalties, providing flexibility in model selection.

The key hyperparameters for SGDClassifier include the regularization strength (alpha), which controls model complexity; the loss function (loss), which determines the type of error minimized during training; the penalty type (penalty), which specifies the regularization method; and the maximum number of iterations (max_iter), which defines how long the model trains.

from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.linear_model import SGDClassifier

# 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 parameter grid
param_grid = {
    'alpha': [0.0001, 0.001, 0.01],
    'loss': ['hinge', 'log', 'squared_hinge'],
    'penalty': ['l2', 'l1', 'elasticnet'],
    'max_iter': [1000, 2000, 3000]
}

# Perform grid search
grid_search = GridSearchCV(estimator=SGDClassifier(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: 0.808
Best parameters: {'alpha': 0.01, 'loss': 'squared_hinge', 'max_iter': 1000, 'penalty': 'l1'}
Test set accuracy: 0.815

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 alpha, loss, penalty, and max_iter hyperparameters.
4. Perform grid search using GridSearchCV, specifying the SGDClassifier 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 and identify the optimal configuration for our SGDClassifier model, ensuring maximum performance.