Scikit-Learn GridSearchCV RandomForestClassifier

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 RandomForestClassifier, a versatile algorithm for classification tasks.

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.

RandomForestClassifier is an ensemble learning method that constructs multiple decision trees and merges them to get a more accurate and stable prediction. The algorithm is robust to overfitting and works well with large datasets.

The key hyperparameters for RandomForestClassifier include the number of trees in the forest (n_estimators), which determines the size of the ensemble; the maximum depth of the trees (max_depth), which controls the complexity of the individual trees; and the number of features to consider for the best split (max_features), which impacts the randomness and performance of the trees.

from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.ensemble import RandomForestClassifier

# Generate synthetic binary classification dataset
X, y = make_classification(n_samples=1000, 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 parameter grid
param_grid = {
    'n_estimators': [10, 50, 100],
    'max_depth': [None, 10, 20, 30],
    'max_features': ['auto', 'sqrt', 'log2']
}

# Perform grid search
grid_search = GridSearchCV(estimator=RandomForestClassifier(random_state=42),
                           param_grid=param_grid,
                           cv=5,
                           scoring='accuracy')
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_
accuracy = best_model.score(X_test, y_test)
print(f"Test set accuracy: {accuracy:.3f}")

Running the example gives an output like:

Best score: 0.907
Best parameters: {'max_depth': None, 'max_features': 'sqrt', 'n_estimators': 50}
Test set accuracy: 0.940

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 parameter grid with different values for n_estimators, max_depth, and max_features hyperparameters.
4. Perform grid search using GridSearchCV, specifying the RandomForestClassifier model, parameter grid, 5-fold cross-validation, and accuracy 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 accuracy.

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 RandomForestClassifier model.