machine-learning-interpretability 开源项目推荐（21 个）

jphall663 /awesome-machine-learning-interpretability

A curated list of awesome responsible machine learning resources.

jphall663 /interpretable_machine_learning_with_python

用于训练可解释机器学习模型、解释机器学习模型，以及调试机器学习模型以提升准确性、减少歧视并增强安全性的技术示例。

h2oai /mli-resources

H2O.ai 机器学习可解释性资源

explainX /explainx

用一行代码解释并调试任何黑盒机器学习模型。

DiegoUsaiUK /Propensity_Modelling

Predicting the Likelihood to Purchase a Financial Product Following a Direct Marketing Campaign

jphall663 /diabetes_use_case

Sample use case for Xavier AI in Healthcare conference: https://www.xavierhealth.org/ai-summit-day2/

SDM-TIB /InterpretME

An interpretable machine learning pipeline over knowledge graphs

jphall663 /hc_ml

Slides, videos and other potentially useful artifacts from various presentations on responsible machine learning.

12wang3 /mllp

The code of AAAI 2020 paper "Transparent Classification with Multilayer Logical Perceptrons and Random Binarization".

navdeep-G /interpretable-ml

Techniques & resources for training interpretable ML models, explaining ML models, and debugging ML models.

h2oai /article-information-2019

Article for Special Edition of Information: Machine Learning with Python

jphall663 /jsm_2018_paper

Paper for 2018 Joint Statistical Meetings: https://ww2.amstat.org/meetings/jsm/2018/onlineprogram/AbstractDetails.cfm?abstractid=329539

poloclub /telegam

TeleGam: Combining Visualization and Verbalization for Interpretable Machine Learning

vanderschaarlab /INVASE

INVASE: Instance-wise Variable Selection . For more details, read the paper "INVASE: Instance-wise Variable Selection using Neural Networks," International Conference on Learning Representations (ICLR), 2019.

hayesall /bn-rule-extraction

Rule Extraction from Bayesian Networks

akifcinar /Machine_Learning_Interpretability

Overview of machine learning interpretation techniques and their implementations

nilsdenter /novelty_value_ml

This project contains the data, code and results used in the paper title "On the relationship of novelty and value in digitalization patents: A machine learning approach".

SDM-TIB /InterpretME_Demo

Demonstration of InterpretME, an interpretable machine learning pipeline

xmlx-io /.github

XMLX GitHub configuration

xmlx-dev /.github

XMLX GitHub configuration