Scikit-Learn RandomizedSearchCV LinearSVC

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 LinearSVC model, 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.

LinearSVC is a linear support vector classifier effective for high-dimensional spaces and text classification problems. The model aims to find the optimal hyperplane that separates the classes in the feature space.

Key hyperparameters for LinearSVC include the regularization parameter (C), which controls model complexity and helps prevent overfitting; the loss function (loss), which determines the loss function used (hinge or squared_hinge); and the dual parameter, which decides the algorithm to solve the dual or primal optimization problem.

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

# 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 the model
model = LinearSVC(random_state=42)

# Define hyperparameter distribution
param_dist = {
    'C': uniform(loc=0.01, scale=10),
    'loss': ['hinge', 'squared_hinge'],
    'dual': [False, True]
}

# Perform random search
random_search = RandomizedSearchCV(estimator=model,
                                   param_distributions=param_dist,
                                   n_iter=100,
                                   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: 0.843
Best parameters: {'C': 1.458948720912231, 'dual': True, 'loss': 'squared_hinge'}
Test set accuracy: 0.840

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, loss, and dual hyperparameters.
4. Perform random search using RandomizedSearchCV, specifying the LinearSVC 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 LinearSVC model.