Configure XGBoost "colsample_bylevel" Parameter

The colsample_bylevel parameter in XGBoost controls the fraction of features (columns) sampled for each level (depth) of the tree. By adjusting colsample_bylevel, you can influence the model’s performance and its ability to generalize.

from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier

# Generate synthetic data
X, y = make_classification(n_samples=1000, n_features=20, n_informative=2, n_redundant=10, random_state=42)

# Split the dataset into training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Initialize the XGBoost classifier with a colsample_bylevel value
model = XGBClassifier(colsample_bylevel=0.8, eval_metric='logloss')

# Fit the model
model.fit(X_train, y_train)

# Make predictions
predictions = model.predict(X_test)

Understanding the “colsample_bylevel” Parameter

The colsample_bylevel parameter determines the fraction of features (columns) to be randomly sampled at each level (depth) of the tree during the model’s training process. It is a regularization technique that can help prevent overfitting by reducing the number of features each level of the tree can access, thus encouraging the model to rely on different subsets of features at different depths. colsample_bylevel accepts values between 0 and 1, with 1 meaning that all features are available for each level. The default value of colsample_bylevel in XGBoost is 1.

Choosing the Right “colsample_bylevel” Value

The value of colsample_bylevel affects the model’s performance and its propensity to overfit:

• Lower colsample_bylevel values introduce more randomness into the training process by limiting the number of features each level of the tree can access. This can help prevent overfitting by reducing the model’s reliance on specific features at each depth. However, setting colsample_bylevel too low may hinder the model’s ability to capture important feature interactions at each level and reduce its overall performance.
• Higher colsample_bylevel values allow each level of the tree to access more features, potentially improving the model’s performance by enabling it to learn from a larger portion of the data at each depth. However, setting colsample_bylevel too high can increase the risk of overfitting, as the model may start to memorize noise in the training data.

Practical Tips

• Use cross-validation to find the optimal colsample_bylevel value that strikes a balance between model performance and overfitting.
• Monitor your model’s performance on a separate validation set to detect signs of overfitting (high training performance, low validation performance).