Scikit-Learn RandomizedSearchCV RandomForestClassifier

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 RandomForestClassifier, a popular ensemble method 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.

RandomForestClassifier is an ensemble method that constructs multiple decision trees and merges them to improve classification accuracy and control overfitting. The model is robust and can handle a large number of input features without overfitting.

Key hyperparameters for RandomForestClassifier include:

• n_estimators: Number of trees in the forest.
• max_depth: Maximum depth of the trees.
• min_samples_split: Minimum number of samples required to split an internal node.
• min_samples_leaf: Minimum number of samples required to be at a leaf node.
• max_features: Number of features to consider when looking for the best split.

from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split, RandomizedSearchCV
from sklearn.ensemble import RandomForestClassifier
from scipy.stats import randint

# Generate synthetic binary classification dataset
X, y = make_classification(n_samples=100, n_features=20, n_informative=10, n_redundant=10, 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 = RandomForestClassifier(random_state=42)

# Define hyperparameter distribution
param_dist = {
    'n_estimators': randint(10, 50),
    'max_depth': randint(1, 20),
    'min_samples_split': randint(2, 20),
    'min_samples_leaf': randint(1, 20),
    'max_features': ['auto', 'sqrt', 'log2']
}

# Perform random search
random_search = RandomizedSearchCV(estimator=model,
                                   param_distributions=param_dist,
                                   n_iter=50,
                                   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: 0.875
Best parameters: {'max_depth': 4, 'max_features': 'log2', 'min_samples_leaf': 2, 'min_samples_split': 13, 'n_estimators': 39}
Test set accuracy: 0.850

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 RandomForestClassifier model.
4. Define the hyperparameter distribution with different values for n_estimators, max_depth, min_samples_split, min_samples_leaf, and max_features.
5. Perform random search using RandomizedSearchCV, specifying the RandomForestClassifier model, hyperparameter distribution, 100 iterations, 5-fold cross-validation, and accuracy scoring metric.
6. Report the best cross-validation score and the 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 RandomForestClassifier.