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Tune XGBoost "colsample_bytree" Parameter

The colsample_bytree parameter in XGBoost controls the fraction of features (columns) sampled for each tree in the model.

It introduces randomness and reduces the correlation between trees, which can help prevent overfitting. By tuning colsample_bytree, you can find the optimal value that balances the model’s ability to capture important features while maintaining diversity among the trees.

This example demonstrates how to tune the colsample_bytree 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=2, 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 = {
    'colsample_bytree': np.arange(0.2, 0.9, 0.1)

# 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 colsample_bytree: {grid_search.best_params_['colsample_bytree']}")
print(f"Best CV accuracy: {grid_search.best_score_:.4f}")

# Plot colsample_bytree vs. accuracy
import matplotlib.pyplot as plt
results = grid_search.cv_results_

plt.figure(figsize=(10, 6))
plt.plot(param_grid['colsample_bytree'], results['mean_test_score'], marker='o', linestyle='-', color='b')
plt.fill_between(param_grid['colsample_bytree'], results['mean_test_score'] - results['std_test_score'],
                 results['mean_test_score'] + results['std_test_score'], alpha=0.1, color='b')
plt.title('Colsample Bytree vs. Accuracy')
plt.xlabel('Colsample Bytree')
plt.ylabel('CV Average Accuracy')

# Train a final model with the best colsample_bytree value
best_colsample_bytree = grid_search.best_params_['colsample_bytree']
final_model = xgb.XGBClassifier(n_estimators=100, learning_rate=0.1, colsample_bytree=best_colsample_bytree, random_state=42)
final_model.fit(X, y)

The resulting plot may look as follows:

xgboost tune colsample_bytree

In this example, we create a synthetic binary 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 range of colsample_bytree values we want to test. In this case, we consider values from 0.2 to 0.8 with a step of 0.1.

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 colsample_bytree 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 colsample_bytree 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 colsample_bytree affects the model’s performance.

Finally, we train a final model using the best colsample_bytree value found during the grid search. This model can be used for making predictions on new data.

By tuning the colsample_bytree hyperparameter using grid search with cross-validation, we can find the optimal value that balances the model’s ability to capture important features while maintaining diversity among the trees. This helps prevent overfitting and improves the model’s generalization performance.

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