2020年1月25日に行われた第83回Tokyo.Rでの発表資料です https://tokyor.connpass.com/event/161709/ 資料で使われたコードは以下になります https://github.com/dropout009/tokyoR83
Limitations of Interpretable Machine Learning Methods 2020-10-05 Preface This book explains limitations of current methods in interpretable machine learning. The methods include partial dependence plots (PDP), Accumulated Local Effects (ALE), permutation feature importance, leave-one-covariate out (LOCO) and local interpretable model-agnostic explanations (LIME). All of those methods can be used t
Interpretable Machine Learning A Guide for Making Black Box Models Explainable Christoph Molnar 2023-08-21 Summary Machine learning has great potential for improving products, processes and research. But computers usually do not explain their predictions which is a barrier to the adoption of machine learning. This book is about making machine learning models and their decisions interpretable. Afte
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