The XGBoost Dart Booster, specified by setting booster='dart', is an alternative to the default Tree Booster (gbtree).
Dart stands for “Dropouts meet Multiple Additive Regression Trees” and is designed to improve the regularization of the model, reducing the risk of overfitting.
Dart Booster works by introducing dropouts during the training process, randomly removing a fraction of the decision trees in each boosting iteration. This helps to prevent the model from relying too heavily on any single tree, thus improving its generalization ability.
Here’s an example demonstrating how to use the Dart Booster for a binary classification task using a synthetic dataset:
import numpy as np
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
from sklearn.metrics import accuracy_score
# Generate a synthetic classification dataset
X, y = make_classification(n_samples=1000, n_features=10, n_classes=2, random_state=42)
# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Initialize an XGBClassifier with dart booster
clf = XGBClassifier(booster='dart', max_depth=5, learning_rate=0.1, n_estimators=100,
sample_type='uniform', normalize_type='tree', rate_drop=0.1, skip_drop=0.5)
# Train the model
clf.fit(X_train, y_train)
# Make predictions on the test set
predictions = clf.predict(X_test)
# Evaluate the model
accuracy = accuracy_score(y_test, predictions)
print(f"Accuracy: {accuracy:.4f}")
In this example, we first generate a synthetic binary classification dataset using make_classification() from scikit-learn. We then split the data into training and testing sets.
Next, we initialize an XGBClassifier with booster='dart' and set several hyperparameters specific to the Dart Booster:
sample_type: The type of sampling algorithm. Can be either ‘uniform’ (default) or ‘weighted’.normalize_type: The type of normalization algorithm. Can be either ’tree’ (default) or ‘forest’.rate_drop: The fraction of trees to drop during each boosting iteration. Default is 0.0.skip_drop: The probability of skipping the dropout procedure during a boosting iteration. Default is 0.0.
We then train the model using the fit() method, make predictions on the test set using predict(), and evaluate the model’s performance using accuracy_score().
When using the Dart Booster, it’s essential to tune the hyperparameters to find the optimal balance between model complexity and generalization. In addition to the Dart-specific parameters, you should also experiment with common XGBoost parameters such as max_depth, learning_rate, and n_estimators.
By leveraging the regularization capabilities of the XGBoost Dart Booster, you can build more robust and generalized models, particularly when dealing with noisy or complex datasets prone to overfitting.