Results 21 to 30 of about 44,463 (307)

SHAP values.

, 2023
Explanations for SHAP Values and the computations behind. (PDF)
Hakim Benkirane (14725564), Paul-Henry Cournède (14004669), Yoann Pradat (14725567), Stefan Michiels (108030)   +3 more
core   +2 more sources

Measuring Impacts of Urban Environmental Elements on Housing Prices Based on Multisource Data—A Case Study of Shanghai, China

ISPRS International Journal of Geo-Information, 2020
Diverse urban environmental elements provide health and amenity value for residents. People are willing to pay a premium for a better environment. Thus, it is essential to assess the benefits and values of these environmental elements.
Liujia Chen, Xiaojing Yao, Yalan Liu, Yujiao Zhu, Wei Chen, Xizhi Zhao, Tianhe Chi   +6 more
doaj   +1 more source

conrad-blucher-institute/shap: CBI SHAP fork: Channel-wise PartitionSHAP

, 2022
For details, see: Krell, Evan; Kamangir, Hamid; Friesen, Joshua; Judge, Julianna; Collins, Waylon; King, Scott A.; Tissot, Philippe (2022). Explaining Complex 3D Atmospheric CNNs Using SHAP-Based Channel-wise XAI Techniques with Interactive 3D ...
kodonnell, Sachin Varghese, John Zedlewski, xzzxxzzx, moritzaugustin, lrjball, Wong Mun Hou, KOLANICH, ihopethiswillfi, Parsa Torabian, Scott Lundberg, anusham1990, mizukasai, Gabriel Tseng, Floid Gilbert, Jeremiah, Jorge Leitao, Jacob McCright, Maggie Wu, Simon Brugman, Jocelin Su, Ilya Matiach, Roma Ushakov, Vivek Chettiar, Quentin Ambard, Jason Tam, Ryan Serrao, Hydrogen Bear, Rory Mitchell   +28 more
core   +1 more source

Global SHAP results.

, 2023
SHAP results for the gradient boosting (XGB) and random forests (RF) best models obtained with the Zones feature set. Left, horizontal bar plots of the SHAP values. Center, violin plots. Right, partial dependence plot for the two most important features.
Monica Hernandez (7584980), Elisa Vilades (3095034), Ubaldo Ramon-Julvez (12511678), Elvira Mayordomo (3819208), Elena Garcia-Martin (3095040), Beatriz Cordon (9745619)   +5 more
core   +2 more sources

SHAP variable importance plots.

, 2022
(A) SHAP values for muscle-skeletal tissue. (B) SHAP values for the lung tissue. The plot includes all samples in the training data and the values represent the impact of the gene on model prediction output. SHAP values explain to what extent the feature
Ehud Sussman (12225565), Judith Somekh (109861), Gur Arye Yehuda (12225568), Nir Lotan (12225562)   +3 more
core   +1 more source

SHAP interpretation.

, 2022
SHAP interpretation.
Yuki Takahashi (1620730), Hiroki Yasuma (13883820), Nobuo Kimura (13883829), Nguyen Minh Khiem (13883817), Khuu Thi Phuong Dong (13883823), Tran Ngoc Hai (13883826)   +5 more
core   +1 more source

A Protocol for Continual Explanation of SHAP

ESANN 2023 proceesdings, 2023
ESANN 2023, 6 pages, added link to ...
Cossu, Andrea, Spinnato, Francesco, Guidotti, Riccardo, Bacciu, Davide   +3 more
openaire   +3 more sources

On the Performance of Machine Learning Based Flight Delay Prediction – Investigating the Impact of Short-Term Features

Promet (Zagreb), 2022
People and companies today are connected around the world, which has led to a growing importance of the aviation industry. As flight delays are a big challenge in aviation, machine learning algorithms can be used to forecast those.
Delia Schösser, Jörn Schönberger
doaj   +1 more source

Machine learning models for predicting the risk factor of carotid plaque in cardiovascular disease

Frontiers in Cardiovascular Medicine, 2023
IntroductionCardiovascular disease (CVD) is a group of diseases involving the heart or blood vessels and represents a leading cause of death and disability worldwide.
Chengling Bin, Qin Li, Jing Tang, Chaorong Dai, Ting Jiang, Xiufang Xie, Min Qiu, Lumiao Chen, Shaorong Yang   +8 more
doaj   +1 more source

SHAP summary plot.

, 2023
A: SHAP summary plots showing the importance of all metabolomic features. B: SHAP summary plot illustration with testosterone glucuronide and p-Anisic acid.
Olatomiwa O. Bifarin (15411649)
core   +1 more source