Hollow-tree super: A directional and scalable approach for feature importance in boosted tree models.

ABSTRACT:

Purpose

Current limitations in methodologies used throughout machine-learning to investigate feature importance in boosted tree modelling prevent the effective scaling to datasets with a large number of features, particularly when one is investigating both the magnitude and directionality of various features on the classification into a positive or negative class. This manuscript presents a novel methodology, "Hollow-tree Super" (HOTS), designed to resolve and visualize feature importance in boosted tree models involving a large number of features. Further, this methodology allows for accurate investigation of the directionality and magnitude various features have on classification and incorporates cross-validation to improve the accuracy and validity of the determined features of

SUBMITTER: Doyen S 

PROVIDER: S-EPMC8544862 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature