XGBoosting Home | About | Contact | Examples

Configure XGBoost "importance_type" Parameter

The importance_type parameter in XGBoost determines the method used to calculate feature importance scores, which are crucial for interpreting the model’s decisions.

By setting the appropriate importance_type, you can gain valuable insights into the relative importance of features in your dataset.

from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor

# Generate synthetic data
X, y = make_regression(n_samples=1000, n_features=20, noise=0.1, 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 regressor with a specific importance_type
model = XGBRegressor(importance_type='gain')

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

# Get feature importance scores

Understanding the “importance_type” Parameter

The importance_type parameter in XGBoost offers several options for calculating feature importance:

Each importance_type provides a different perspective on the significance of features in the model.

Choosing the Right “importance_type”

The choice of importance_type depends on the problem and the desired interpretation of feature importance:

Keep in mind that the choice of importance_type can affect the ranking of features, so it’s essential to select the appropriate method based on your specific problem and desired interpretation.

Practical Tips

See Also