1 code implementation • 27 Mar 2020 • Yusuke Kubo, Yuto Komori, Toyonobu Okuyama, Hiroshi Tokieda
Interpretability of machine learning is defined as the extent to which humans can comprehend the reason of a decision.
no code implementations • 22 Jun 2019 • Keiichi Kisamori, Keisuke Yamazaki, Yuto Komori, Hiroshi Tokieda
One approach is replacing the un-interpretable machine learning model with a surrogate model, which has a simple structure for interpretation.