Scikit-Learn RandomizedSearchCV NuSVC

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 NuSVC model, commonly used for binary 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.

NuSVC is a variant of Support Vector Classification (SVC) which uses a parameter ’nu’ to control the number of support vectors and margin errors. This allows for flexibility in handling outliers and balancing the trade-off between margin and classification error.

Key hyperparameters for NuSVC include the nu parameter, which is an upper bound on the fraction of margin errors and a lower bound on the fraction of support vectors; the kernel type, which can be linear, polynomial, radial basis function (RBF), or sigmoid; and gamma, the kernel coefficient for RBF, polynomial, and sigmoid kernels.

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

# Generate synthetic binary classification dataset
X, y = make_classification(n_samples=1000, n_features=10, n_informative=5, n_redundant=5, 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 = NuSVC(random_state=42)

# Define hyperparameter distribution
param_dist = {
    'nu': uniform(0.1, 0.9),
    'kernel': ['linear', 'rbf', 'poly', 'sigmoid'],
    'gamma': uniform(0.001, 1)
}

# Perform random search
random_search = RandomizedSearchCV(estimator=model,
                                   param_distributions=param_dist,
                                   n_iter=50,
                                   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: 0.933
Best parameters: {'gamma': 0.389677289689482, 'kernel': 'rbf', 'nu': 0.5857229191501718}
Test set accuracy: 0.925

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 NuSVC model.
4. Define the hyperparameter distribution for nu, kernel, and gamma.
5. Perform random search using RandomizedSearchCV, specifying the NuSVC model, hyperparameter distribution, 100 iterations, 5-fold cross-validation, and accuracy scoring metric.
6. Report the best cross-validation score and best set of hyperparameters found by random search.
7. 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 NuSVC model.