Scikit-Learn RandomizedSearchCV NearestCentroid

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 NearestCentroid 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.

NearestCentroid is a simple and efficient classification algorithm that assigns a class label based on the closest centroid of the training samples. It’s particularly effective for small datasets with distinct clusters.

Key hyperparameters for NearestCentroid include the shrink_threshold, which adjusts the centroids by shrinking them towards zero. This can help in handling datasets with high dimensionality or noisy features.

from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split, RandomizedSearchCV
from sklearn.neighbors import NearestCentroid
from scipy.stats import uniform

# Generate synthetic classification dataset
X, y = make_classification(n_samples=1000, n_features=20, n_informative=15, 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 = NearestCentroid()

# Define hyperparameter distribution
param_dist = {
    'shrink_threshold': uniform(0, 1)
}

# 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: 0.682
Best parameters: {'shrink_threshold': 0.5986584841970366}
Test set accuracy: 0.675

The steps are as follows:

1. Generate a synthetic 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 shrink_threshold.
4. Perform random search using RandomizedSearchCV, specifying the NearestCentroid 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 NearestCentroid model.