Scikit-Learn RandomizedSearchCV ExtraTreeClassifier

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 an ExtraTreeClassifier, a decision tree classifier variant.

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 ExtraTreeClassifier is a type of decision tree classifier that constructs trees from entire samples of data. This makes it faster but potentially less robust to overfitting compared to other tree-based models.

Key hyperparameters for ExtraTreeClassifier include the maximum depth (max_depth), which limits the depth of the tree to prevent overfitting; the minimum samples required to split a node (min_samples_split), which helps control the size of the trees; and the number of features to consider for the best split (max_features), which affects the randomness of the tree construction.

from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split, RandomizedSearchCV
from sklearn.tree import ExtraTreeClassifier
from scipy.stats import randint

# 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 the model
model = ExtraTreeClassifier(random_state=42)

# Define hyperparameter distribution
param_dist = {
    'max_depth': randint(1, 20),
    'min_samples_split': randint(2, 20),
    'max_features': randint(1, 20)
}

# 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: 0.829
Best parameters: {'max_depth': 12, 'max_features': 12, 'min_samples_split': 18}
Test set accuracy: 0.880

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 model then the hyperparameter distribution with different values for max_depth, min_samples_split, and max_features hyperparameters.
4. Perform random search using RandomizedSearchCV, specifying the ExtraTreeClassifier 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 ExtraTreeClassifier model.