Scikit-Learn GridSearchCV SVC

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 Classification (SVC), a powerful 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.

Support Vector Classification (SVC) is a powerful classifier that finds a hyperplane to separate different classes in the feature space. It is effective in high-dimensional spaces and versatile due to different kernel functions.

The key hyperparameters for SVC include the regularization parameter C, which controls the trade-off between achieving a low training error and a low testing error; the kernel type, which specifies the kernel function to be used in the algorithm (e.g., linear, polynomial, radial basis function (RBF)); and gamma, the kernel coefficient for ‘rbf’, ‘poly’, and ‘sigmoid’ kernels.

from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.svm import SVC

# 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 parameter grid
param_grid = {
    'C': [0.1, 1, 10, 100],
    'kernel': ['linear', 'poly', 'rbf', 'sigmoid'],
    'gamma': ['scale', 'auto']
}

# Perform grid search
grid_search = GridSearchCV(estimator=SVC(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: 0.940
Best parameters: {'C': 10, 'gamma': 'scale', 'kernel': 'rbf'}
Test set accuracy: 0.965

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 C, kernel, and gamma.
4. Perform grid search using GridSearchCV, specifying the SVC 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 systematically explore different hyperparameter settings and identify the combination that maximizes the model’s performance. This approach automates the hyperparameter tuning process, saving time and ensuring optimal model configuration for SVC.