A Robust Method to Measure the Global Feature Importance of Complex Prediction Models

A Robust Method to Measure the Global Feature Importance of Complex Prediction Models

Because machine learning has been widely used in various domains, interpreting internal mechanisms and predictive results of models is crucial for further applications of complex machine learning models. However, the interpretability of complex machine learning models on biased data remains a diffic...

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Bibliographic Details
Main Authors: Xiaohang Zhang, Ling Wu, Zhengren Li, Huayuan Liu
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Feature importance
global interpretation
high-dimensional model representation
robustness
supervised machine learning
Online Access:https://ieeexplore.ieee.org/document/9314116/

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