Scikit-Learn GridSearchCV RadiusNeighborsRegressor

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 RadiusNeighborsRegressor, a regression algorithm that uses nearby points within a fixed radius.

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.

RadiusNeighborsRegressor predicts the target value based on the average of neighboring points within a given radius. The model is particularly useful for regression tasks where the relationship between features and the target variable is non-linear and localized.

The key hyperparameters for RadiusNeighborsRegressor include the radius, which specifies the radius of the neighborhood; the weights, which determines the weight function used in prediction; and the algorithm, which specifies the algorithm used to compute the nearest neighbors.

from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.neighbors import RadiusNeighborsRegressor

# Generate synthetic regression dataset
X, y = make_regression(n_samples=1000, n_features=10, noise=0.1, 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=RadiusNeighborsRegressor(),
                           param_grid=param_grid,
                           cv=5,
                           scoring='neg_mean_squared_error')
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_
test_score = best_model.score(X_test, y_test)
print(f"Test set score: {test_score:.3f}")

Running the example gives an output like:

Best score: -15978.129
Best parameters: {'algorithm': 'ball_tree', 'radius': 10, 'weights': 'distance'}
Test set score: 0.081

The steps are as follows:

1. Generate a synthetic regression dataset using scikit-learn’s make_regression function.
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 RadiusNeighborsRegressor model, parameter grid, 5-fold cross-validation, and negative mean squared error 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 score.

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 RadiusNeighborsRegressor model.