Scikit-Learn RandomizedSearchCV KNeighborsRegressor

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 KNeighborsRegressor model, commonly used for regression 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.

KNeighborsRegressor is a non-parametric regression method that predicts the target value based on the average of the k-nearest neighbors’ target values. The model’s predictions are determined by the proximity of the input data points in the feature space.

Key hyperparameters for KNeighborsRegressor include:

• n_neighbors: The number of neighbors to use.
• weights: The weight function used in prediction, which can be ‘uniform’ or ‘distance’.
• algorithm: The algorithm used to compute the nearest neighbors, which can be ‘auto’, ‘ball_tree’, ‘kd_tree’, or ‘brute’.

from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split, RandomizedSearchCV
from sklearn.neighbors import KNeighborsRegressor
from scipy.stats import randint

# 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 the model
model = KNeighborsRegressor()

# Define hyperparameter distribution
param_dist = {
    'n_neighbors': randint(1, 30),
    'weights': ['uniform', 'distance'],
    'algorithm': ['auto', 'ball_tree', 'kd_tree', 'brute']
}

# Perform random search
random_search = RandomizedSearchCV(estimator=model,
                                   param_distributions=param_dist,
                                   n_iter=100,
                                   cv=5,
                                   scoring='r2',
                                   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_
r2_score = best_model.score(X_test, y_test)
print(f"Test set R^2 score: {r2_score:.3f}")

Running the example gives an output like:

Best score: 0.766
Best parameters: {'algorithm': 'brute', 'n_neighbors': 6, 'weights': 'distance'}
Test set R^2 score: 0.790

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 KNeighborsRegressor model.
4. Define the hyperparameter distributions with different values for n_neighbors, weights, and algorithm.
5. Perform random search using RandomizedSearchCV, specifying the KNeighborsRegressor model, hyperparameter distribution, 100 iterations, 5-fold cross-validation, and R^2 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 R^2 score.

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 KNeighborsRegressor model.