Scikit-Learn RandomizedSearchCV SVC

Hyperparameter tuning is essential for optimizing machine learning models. In this example, we’ll demonstrate how to use scikit-learn’s RandomizedSearchCV for hyperparameter tuning of a Support Vector Classifier (SVC), commonly used for classification tasks.

Random search is a method for evaluating different combinations of model hyperparameters. Unlike grid search, it samples a fixed number of hyperparameter combinations from a specified distribution, making it more efficient when searching over a large hyperparameter space.

Support Vector Classifier (SVC) is a type of Support Vector Machine (SVM) used for classification tasks. It finds the hyperplane that best separates the classes by maximizing the margin between data points of different classes.

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 function to be used for the kernel trick (e.g., ’linear’, ‘poly’, ‘rbf’); and the kernel coefficient (gamma), which determines the influence of a single training example.

from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split, RandomizedSearchCV
from sklearn.svm import SVC
from scipy.stats import expon, uniform

# Generate synthetic binary classification dataset
X, y = make_classification(n_samples=100, 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 the model
model = SVC(random_state=42)

# Define hyperparameter distribution
param_dist = {
    'C': expon(scale=100),
    'kernel': ['linear', 'poly', 'rbf'],
    'gamma': uniform(scale=0.1)
}

# Perform random search
random_search = RandomizedSearchCV(estimator=model,
                                   param_distributions=param_dist,
                                   n_iter=10,
                                   cv=5,
                                   scoring='accuracy',
                                   random_state=42)
random_search.fit(X_train, y_train)

# Report best score and parameters
print(f"Best score: {random_search.best_score_:.3f}")
print(f"Best parameters: {random_search.best_params_}")

# Evaluate on test set
best_model = random_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: 0.887
Best parameters: {'C': 51.05939367995812, 'gamma': 0.004666566321361543, 'kernel': 'rbf'}
Test set accuracy: 0.600

The steps are as follows:

1. Generate a synthetic binary classification dataset using scikit-learn’s make_classification function.
2. Split the dataset into train and test sets using train_test_split.
3. Define the model then the hyperparameter distribution with different values for C, kernel, and gamma hyperparameters.
4. Perform random search using RandomizedSearchCV, specifying the SVC model, hyperparameter distribution, 100 iterations, 5-fold cross-validation, and accuracy scoring metric.
5. Report the best cross-validation score and best set of hyperparameters found by random search.
6. Evaluate the best model on the hold-out test set and report the accuracy.

By using RandomizedSearchCV, we can efficiently 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 Support Vector Classifier.