Configure XGBoost "sampling_method" Parameter

The sampling_method parameter in XGBoost plays a critical role in how training data is sampled when building trees.

Proper configuration of this parameter can lead to improvements in training speed and model accuracy, making it a vital aspect for fine-tuning your XGBoost models.

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

# 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 specific sampling method
model = XGBClassifier(sampling_method='uniform', subsample=0.5, eval_metric='logloss')

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

# Make predictions
predictions = model.predict(X_test)

The default sampling_method is 'uniform'. It can only be changed to 'gradient_based' if tree_method is set to 'hist' and device is set to 'cuda'.

Otherwise you will get an error like: “Only uniform sampling is supported, gradient-based sampling is only support by GPU Hist”.

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

# 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 specific sampling method
model = XGBClassifier(tree_method='hist', device='cuda', sampling_method='gradient_based', subsample=0.5, eval_metric='logloss')

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

# Make predictions
predictions = model.predict(X_test)

Understanding the “sampling_method” Parameter

The sampling_method parameter in XGBoost determines how instances are sampled during the tree construction phase. This can significantly influence the behavior of the algorithm during training:

• uniform: This method samples instances randomly and equally, giving each instance the same probability of being selected. It is straightforward and can be effective for datasets where every sample is similarly important.
• gradient_based: This approach prioritizes instances based on the magnitude of their gradients, meaning instances with higher errors (and thus steeper gradients) are more likely to be selected. This method is particularly useful for complex datasets with imbalanced classes or significant noise.

Choosing the Right “sampling_method” Value

Selecting the appropriate sampling method depends on your dataset and the specific challenges it presents:

• Use uniform if:

  • Your dataset is relatively noise-free and well-balanced.
  • Each instance is of roughly equal importance to the learning process.

• Opt for gradient_based when:

  • Dealing with imbalanced datasets where prioritizing misclassified instances can drive better overall performance.
  • You aim to focus on the most challenging instances during training, potentially improving the robustness and accuracy of your model.

Practical Tips

• Experimentation is key: Try both sampling methods on a subset of your data to determine which yields better performance metrics.
• Monitor resources: The gradient_based method might increase computational costs due to its dynamic nature. Keep an eye on the training time and resource usage.
• Validate changes: Use cross-validation to assess the impact of the sampling method on your model’s performance. This will help ensure that your adjustments lead to genuine improvements rather than fitting to noise or specific idiosyncrasies of your training data.