Christoph Molnaris a data scientist and PhD candidate in interpretable machine learning. Molnar has written the book"Interpretable Machine Learning: A Guide for Making Black Box Models Explainable", in which he elaborates on the issue and examines methods for achieving expla...
之后的章节里关注于一般的模型无关的方法来解释黑盒模型,比如特征重要性【例如:Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks,数据内部表示中的一部分表示相比其他表示更加transferrable,即这些表示在其他任务分布p(T)中都具有广泛的适用性,而非只在一个任务中有效。】、累积的局部效应,以及用Shap...
nlpfastmachine-learningdeep-learningsimpletensorflowmodelword-embeddingskerasgloveexplainableswemglobal-explanationlocal-explanation UpdatedFeb 7, 2021 Jupyter Notebook A runtime monitoring tool that produces explanations as verdicts runtime-monitoringexplainabletemporal-logics ...
3D Point Cloud Analysis: Traditional, Deep Learning, and Explainable Machine Learning Methods的书评 ··· ( 全部0 条 ) 论坛 ··· 在这本书的论坛里发言 + 加入购书单 以下书单推荐 ··· ( 全部 ) T (dhcn) 计算机技术|2023年-研1 (静观然) 谁读这本书? ··· 小狐狸爱吃刺身...
Cox Proportional Hazards (CPH) analysis is the standard for survival analysis in oncology. Recently, several machine learning (ML) techniques have been adapted for this task. Although they have shown to yield results at least as good as classical methods
之后的章节里关注于一般的模型无关的方法来解释黑盒模型,比如特征重要性【例如:Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks,数据内部表示中的一部分表示相比其他表示更加transferrable,即这些表示在其他任务分布p(T)中都具有广泛的适用性,而非只在一个任务中有效。】、累积的局部效应,以及用...
Interpretable Machine Learning is a comprehensive guide to making machine learning models interpretable “Pretty convinced this is the best book out there on the subject” –Brian Lewis, Data Scientist at Cornerstone Research Summary This book covers a range of interpretability methods, from inherently ...
Overview of explainable machine learning framework for CVD risk prediction. Full size image Data description This study uses data from a nationally representative sample of adolescents who participated in the National Longitudinal Study of Adolescent to Adult Health (Add Health)29. The study followed ov...
This book provides a full presentation of the current concepts and available techniques to make "machine learning" systems more explainable. The approaches presented can be applied to almost all the current "machine learning" models: linear and logistic regression, deep learning neural networks, natura...
The experimental database used in the current study and the Jupyter Notebook Python code for the machine learning and SHAP model are provided on GitHub (https://github.com/sujithmangalathu/Punching_shear_flat_slab). CRediT authorship contribution statement Sujith Mangalathu: Conceptualization, Methodol...