Scikit-Learn RandomizedSearchCV KNeighborsClassifier

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 KNeighborsClassifier 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.

KNeighborsClassifier is a non-parametric method used for classification. It assigns the class based on the majority class among the k-nearest neighbors in the feature space. The model’s performance can be significantly influenced by the choice of hyperparameters.

Key hyperparameters for KNeighborsClassifier include the number of neighbors (n_neighbors), which determines how many neighbors to use for making predictions; the weight function (weights), which affects how the influence of neighbors is calculated; and the distance metric (metric), which defines how distances between points are computed.

from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split, RandomizedSearchCV
from sklearn.neighbors import KNeighborsClassifier
from scipy.stats import randint

# Generate synthetic 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 = KNeighborsClassifier()

# Define hyperparameter distribution
param_dist = {
    'n_neighbors': randint(1, 30),
    'weights': ['uniform', 'distance'],
    'metric': ['euclidean', 'manhattan', 'minkowski']
}

# 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: 0.948
Best parameters: {'metric': 'minkowski', 'n_neighbors': 9, 'weights': 'distance'}
Test set accuracy: 0.945

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 n_neighbors, weights, and metric hyperparameters.
4. Perform random search using RandomizedSearchCV, specifying the KNeighborsClassifier 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 KNeighborsClassifier model.