Scikit-Learn RandomizedSearchCV DecisionTreeClassifier

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 DecisionTreeClassifier, a popular choice 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.

DecisionTreeClassifier is a model used for classification tasks by splitting the data based on feature values. The model creates a tree where each node represents a feature, each branch represents a decision rule, and each leaf represents an outcome.

Key hyperparameters for DecisionTreeClassifier include max_depth, which limits the depth of the tree to prevent overfitting; min_samples_split, which is the minimum number of samples required to split an internal node; and min_samples_leaf, which is the minimum number of samples required to be at a leaf node.

from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split, RandomizedSearchCV
from sklearn.tree import DecisionTreeClassifier
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 = DecisionTreeClassifier(random_state=42)

# Define hyperparameter distribution
param_dist = {
    'max_depth': randint(1, 20),
    'min_samples_split': randint(2, 20),
    'min_samples_leaf': 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: 0.845
Best parameters: {'max_depth': 14, 'min_samples_leaf': 3, 'min_samples_split': 2}
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 DecisionTreeClassifier model.
4. Specify the hyperparameter distribution with different values for max_depth, min_samples_split, and min_samples_leaf.
5. Perform random search using RandomizedSearchCV, specifying the DecisionTreeClassifier 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 DecisionTreeClassifier model.