The effect of different feature selection methods on models created with XGBoost
This addresses the problem of feature selection efficiency for practitioners using XGBoost, but it is incremental as it tests existing methods on a known algorithm.
The study investigated how different feature selection methods affect XGBoost models, finding that dimensionality reduction did not significantly change prediction accuracy, suggesting overfitting concerns may not apply to XGBoost but computational benefits remain.
This study examines the effect that different feature selection methods have on models created with XGBoost, a popular machine learning algorithm with superb regularization methods. It shows that three different ways for reducing the dimensionality of features produces no statistically significant change in the prediction accuracy of the model. This suggests that the traditional idea of removing the noisy training data to make sure models do not overfit may not apply to XGBoost. But it may still be viable in order to reduce computational complexity.