Scikit-Learn GridSearchCV SVR

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 Support Vector Regression (SVR), a popular algorithm for 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.

Support Vector Regression (SVR) is an extension of support vector machines (SVM) for regression tasks. It tries to fit the best line within a margin of tolerance (epsilon).

The key hyperparameters for SVR include:

• C: Regularization parameter. Controls trade-off between achieving a low error on training data and minimizing model complexity.
• epsilon: Defines the margin of tolerance where no penalty is given to errors.
• kernel: Specifies the kernel type to be used in the algorithm.

from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.svm import SVR

# 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 = {
    'C': [0.1, 1, 10],
    'epsilon': [0.01, 0.1, 1],
    'kernel': ['linear', 'rbf']
}

# Perform grid search
grid_search = GridSearchCV(estimator=SVR(),
                           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_
test_score = best_model.score(X_test, y_test)
print(f"Test set R^2 score: {test_score:.3f}")

Running the example gives an output like:

Best score: -0.010
Best parameters: {'C': 10, 'epsilon': 0.01, 'kernel': 'linear'}
Test set R^2 score: 1.000

The steps are as follows:

1. Generate a synthetic regression dataset using scikit-learn’s make_regression function.
2. Split the dataset into train and test sets using train_test_split.
3. Define the parameter grid with different values for C, epsilon, and kernel hyperparameters.
4. Perform grid search using GridSearchCV, specifying the SVR model, parameter grid, 5-fold cross-validation, and negative 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 R^2 score.

By using GridSearchCV, we can 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 SVR model.