Scikit-Learn GridSearchCV SGDRegressor

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 SGDRegressor, a popular algorithm for linear regression 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.

SGDRegressor is a linear model trained with stochastic gradient descent, suitable for large-scale and sparse machine learning problems. It iteratively optimizes the model parameters by minimizing the loss function using a gradient-based approach.

The key hyperparameters for SGDRegressor include the regularization term alpha, which prevents overfitting by controlling the model complexity; the penalty type (l1, l2, or elasticnet), which determines the type of regularization applied; and the max_iter, which sets the maximum number of iterations over the training data.

from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.linear_model import SGDRegressor

# Generate synthetic regression dataset
X, y = make_regression(n_samples=1000, n_features=10, noise=0.1, 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],
    'penalty': ['l1', 'l2', 'elasticnet'],
    'max_iter': [1000, 2000, 3000]
}

# Perform grid search
grid_search = GridSearchCV(estimator=SGDRegressor(random_state=42),
                           param_grid=param_grid,
                           cv=5,
                           scoring='neg_mean_squared_error')
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_
score = best_model.score(X_test, y_test)
print(f"Test set score: {score:.3f}")

Running the example gives an output like:

Best score: -0.010
Best parameters: {'alpha': 0.0001, 'max_iter': 1000, 'penalty': 'l1'}
Test set score: 1.000

The steps are as follows:

1. Generate a synthetic regression dataset using make_regression.
2. Split the dataset into train and test sets using train_test_split.
3. Define the parameter grid with different values for alpha, penalty, and max_iter hyperparameters.
4. Perform grid search using GridSearchCV, specifying the SGDRegressor model, parameter grid, 5-fold cross-validation, and neg_mean_squared_error 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 score.

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 SGDRegressor model.