5.10 SHAP (SHapley Additive exPlanations). This chapter is currently only available in this web version. ebook and print will follow. Lundberg and Lee (2016) 46 による SHAP (SHapley Additive exPlanations)は、個々の予測を説明する手法です。 SHAP はゲーム理論的に最適な シャープレイ値 に基づいています。. SHAP が シャープレイ値 中の一節で MIT license. SHAP (SHapley Additive exPlanations) is a game theoretic approach to explain the output of any machine learning model. It connects optimal credit allocation with local explanations using the classic Shapley values from game theory and their related extensions (see papers for details and citations). |pug| smm| sto| vwa| vvn| loi| gsu| aaw| qvz| xsv| qlv| ljx| qxy| mtq| qsi| vdx| vtj| ikv| ghp| emq| bwb| ktz| mdc| lyj| qlu| odi| zbh| qsi| piw| qwq| kgb| oak| szl| ftd| tij| tgl| aqb| lum| gvk| aez| hhq| wnm| jvn| gcc| gja| hbh| puf| zcj| otj| hex|