Scikit-Learn RandomizedSearchCV VotingClassifier

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 VotingClassifier model, an ensemble technique that combines multiple classifiers to improve performance.

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.

The VotingClassifier is an ensemble model that combines multiple machine learning classifiers. It can make predictions based on the majority vote of its constituent classifiers (hard voting) or based on the average predicted probabilities (soft voting). This approach leverages the strengths of each individual classifier to enhance overall performance.

Key hyperparameters for the VotingClassifier include:

• estimators: A list of base estimators (e.g., LogisticRegression, RandomForestClassifier) used to form the ensemble.
• voting: The voting strategy, either ‘hard’ for majority voting or ‘soft’ for weighted average probabilities.

from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split, RandomizedSearchCV
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier, VotingClassifier
from scipy.stats import uniform, randint

# Generate synthetic binary 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 base models
logistic = LogisticRegression(random_state=42)
forest = RandomForestClassifier(random_state=42)

# Define the ensemble model
voting_clf = VotingClassifier(estimators=[
    ('logistic', logistic),
    ('forest', forest)
], voting='soft')

# Define hyperparameter distribution
param_dist = {
    'logistic__C': uniform(loc=0.01, scale=10),
    'logistic__penalty': ['l1', 'l2'],
    'forest__n_estimators': randint(10, 200),
    'forest__max_features': ['auto', 'sqrt', 'log2']
}

# Perform random search
random_search = RandomizedSearchCV(estimator=voting_clf,
                                   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: 0.787
Best parameters: {'forest__max_features': 'log2', 'forest__n_estimators': 31, 'logistic__C': 0.5741157902710026, 'logistic__penalty': 'l2'}
Test set accuracy: 0.650

The steps are as follows:

1. Generate a synthetic binary 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 VotingClassifier with LogisticRegression and RandomForestClassifier as base estimators.
4. Define the hyperparameter distributions for LogisticRegression (C and penalty) and RandomForestClassifier (n_estimators and max_features).
5. Perform random search using RandomizedSearchCV, specifying the VotingClassifier model, hyperparameter distribution, 100 iterations, 5-fold cross-validation, and accuracy 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 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 VotingClassifier model.