The max_leaves parameter in XGBoost controls the maximum number of leaf nodes allowed in each tree.
It impacts the model’s complexity and can affect its ability to fit the training data.
By tuning max_leaves, you can find the optimal value that balances the model’s capacity to capture complex patterns while avoiding overfitting.
This example demonstrates how to tune the max_leaves hyperparameter using grid search with cross-validation to find the best value for your model.
import xgboost as xgb
import numpy as np
from sklearn.datasets import make_classification
from sklearn.model_selection import GridSearchCV, StratifiedKFold
from sklearn.metrics import accuracy_score
# Create a synthetic dataset
X, y = make_classification(n_samples=1000, n_classes=3, n_features=20, n_informative=10, random_state=42)
# Configure cross-validation
cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)
# Define hyperparameter grid
param_grid = {
'max_leaves': [10, 20, 30, 40, 50]
}
# Set up XGBoost classifier
model = xgb.XGBClassifier(n_estimators=100, learning_rate=0.1, random_state=42)
# Perform grid search
grid_search = GridSearchCV(estimator=model, param_grid=param_grid, cv=cv, scoring='accuracy', n_jobs=-1, verbose=1)
grid_search.fit(X, y)
# Get results
print(f"Best max_leaves: {grid_search.best_params_['max_leaves']}")
print(f"Best CV accuracy: {grid_search.best_score_:.4f}")
# Plot max_leaves vs. accuracy
import matplotlib.pyplot as plt
results = grid_search.cv_results_
plt.figure(figsize=(10, 6))
plt.plot(param_grid['max_leaves'], results['mean_test_score'], marker='o', linestyle='-', color='b')
plt.fill_between(param_grid['max_leaves'], results['mean_test_score'] - results['std_test_score'],
results['mean_test_score'] + results['std_test_score'], alpha=0.1, color='b')
plt.title('Max Leaves vs. Accuracy')
plt.xlabel('Max Leaves')
plt.ylabel('CV Average Accuracy')
plt.grid(True)
plt.show()
# Train a final model with the best max_leaves value
best_max_leaves = grid_search.best_params_['max_leaves']
final_model = xgb.XGBClassifier(n_estimators=100, learning_rate=0.1, max_leaves=best_max_leaves, random_state=42)
final_model.fit(X, y)
The resulting plot may look as follows:
In this example, we create a synthetic multi-class classification dataset using scikit-learn’s make_classification function. We then set up a StratifiedKFold cross-validation object to ensure that the class distribution is preserved in each fold.
We define a hyperparameter grid param_grid that specifies the values of max_leaves we want to test. In this case, we consider values of 10, 20, 30, 40, and 50.
We create an instance of the XGBClassifier with some basic hyperparameters set, such as n_estimators and learning_rate. We then perform the grid search using GridSearchCV, providing the model, parameter grid, cross-validation object, scoring metric (accuracy), and the number of CPU cores to use for parallel computation.
After fitting the grid search object with grid_search.fit(X, y), we can access the best max_leaves value and the corresponding best cross-validation accuracy using grid_search.best_params_ and grid_search.best_score_, respectively.
We plot the relationship between the max_leaves values and the cross-validation average accuracy scores using matplotlib. We retrieve the results from grid_search.cv_results_ and plot the mean accuracy scores along with the standard deviation as error bars. This visualization helps us understand how the choice of max_leaves affects the model’s performance.
Finally, we train a final model using the best max_leaves value found during the grid search. This model can be used for making predictions on new data.
By tuning the max_leaves hyperparameter using grid search with cross-validation, we can find the optimal value that balances the model’s capacity to capture complex patterns while avoiding overfitting. This helps improve the model’s generalization performance on unseen data.