Scikit-Learn GridSearchCV LinearSVR

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 LinearSVR, a linear Support Vector Regression model used for predicting continuous values.

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.

LinearSVR is a linear model that predicts continuous outcomes. It minimizes the squared error loss and uses regularization to prevent overfitting. The key hyperparameters for LinearSVR include the regularization strength (C), which controls model complexity; epsilon, which defines a tube within which no penalty is associated with the training loss function; and loss, which specifies the loss function used.

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

# 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': [0.1, 0.2, 0.5],
    'loss': ['epsilon_insensitive', 'squared_epsilon_insensitive']
}

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

Running the example gives an output like:

Best score: -0.011
Best parameters: {'C': 10, 'epsilon': 0.1, 'loss': 'epsilon_insensitive'}
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 C, epsilon, and loss hyperparameters.
4. Perform grid search using GridSearchCV, specifying the LinearSVR 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 score.

By using GridSearchCV, we can efficiently explore different hyperparameter settings and find the combination that maximizes the performance of our LinearSVR model. This automated approach streamlines the hyperparameter tuning process and helps ensure optimal model configuration.