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.