all AI news
An empirical study of the effect of background data size on the stability of SHapley Additive exPlanations (SHAP) for deep learning models. (arXiv:2204.11351v2 [cs.LG] UPDATED)
April 28, 2022, 1:12 a.m. | Han Yuan, Mingxuan Liu, Lican Kang, Chenkui Miao, Ying Wu
cs.LG updates on arXiv.org arxiv.org
Nowadays, the interpretation of why a machine learning (ML) model makes
certain inferences is as crucial as the accuracy of such inferences. Some ML
models like the decision tree possess inherent interpretability that can be
directly comprehended by humans. Others like artificial neural networks (ANN),
however, rely on external methods to uncover the deduction mechanism. SHapley
Additive exPlanations (SHAP) is one of such external methods, which requires a
background dataset when interpreting ANNs. Generally, a background dataset
consists of instances …
More from arxiv.org / cs.LG updates on arXiv.org
Jobs in AI, ML, Big Data
Senior ML Researcher - 3D Geometry Processing | 3D Shape Generation | 3D Mesh Data
@ Promaton | Europe
Analytics Engineer
@ CircleCI | Remote (US), Remote (Canada), San Francisco, Denver
Bilingual Executive Assistant/Data Analyst - (French and English) - Export
@ Dangote Group | Lagos, Lagos, Nigeria
Workday Services Data Lead
@ WPP | Mexico City, Mexico
Business Data Analyst
@ Nordea | Tallinn, EE, 11415
Data Integrity Lead
@ BioNTech SE | Gaithersburg, MD, US, MD 20878