Scikit-Learn GridSearchCV RadiusNeighborsClassifier

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 RadiusNeighborsClassifier, 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.

RadiusNeighborsClassifier is a non-linear classification algorithm that assigns a class to a sample based on the majority class among its neighbors within a specified radius. This classifier is particularly useful for datasets with variable density.

The key hyperparameters for RadiusNeighborsClassifier include the radius, which defines the neighborhood size; the weights parameter, which determines how the neighbors’ votes are weighted (e.g., ‘uniform’ or ‘distance’); and the algorithm parameter, which selects the underlying algorithm for computing nearest neighbors (e.g., ‘ball_tree’, ‘kd_tree’, or ‘brute’).

from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.neighbors import RadiusNeighborsClassifier

# 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 parameter grid
param_grid = {
    'radius': [1, 10, 100],
    'weights': ['uniform', 'distance'],
    'algorithm': ['ball_tree', 'kd_tree', 'brute']
}

# Perform grid search
grid_search = GridSearchCV(estimator=RadiusNeighborsClassifier(),
                           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: 0.869
Best parameters: {'algorithm': 'ball_tree', 'radius': 100, 'weights': 'distance'}
Test set accuracy: 0.850

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 radius, weights, and algorithm hyperparameters.
4. Perform grid search using GridSearchCV, specifying the RadiusNeighborsClassifier 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 easily 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 RadiusNeighborsClassifier model.