Which is NOT true of gradient boosting machine (GBM) models?
A) GBM can be implemented under the "caret" package.
B) Decision trees and random forests can be base learning methods under GBM.
C) OLS regression can be a base learner model under GBM.
D) The GBM solution is attained in an iterative process weighting cases differentially, with gradual shifts between solutions.
E) All of the above are true.

Question

Quizplus · Accepted Answer

All the statements provided are true regarding gradient boosting machine (GBM) models. GBM can indeed be implemented under the "caret" package in R, which is a comprehensive framework for building machine learning models. Both decision trees and random forests can serve as base learners in GBM, as GBM is a flexible algorithm that can utilize various types of models as its base learners, including decision trees which are the most common. OLS (Ordinary Least Squares) regression can also be used as a base learner in GBM, especially for regression tasks. Lastly, the iterative process of GBM involves weighting cases differentially to gradually improve the model's accuracy, which correctly describes how GBM optimizes its solution.

Which Is NOT True of Gradient Boosting Machine (GBM) Models