machine-learning-interpretability
There are 21 repositories under machine-learning-interpretability topic.
jphall663/awesome-machine-learning-interpretability
A curated list of awesome responsible machine learning resources.
jphall663/interpretable_machine_learning_with_python
Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.
h2oai/mli-resources
H2O.ai Machine Learning Interpretability Resources
explainX/explainx
Explainable AI framework for data scientists. Explain & debug any blackbox machine learning model with a single line of code. We are looking for co-authors to take this project forward. Reach out @ ms8909@nyu.edu
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
hayesall/bn-rule-extraction
Rule Extraction from Bayesian Networks
akifcinar/Machine_Learning_Interpretability
Overview of machine learning interpretation techniques and their implementations
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.
navdeep-G/fair-ml
Fairness in AI and Machine Learning
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
tommykangdra/Credit-Default-Risk
Default Risk Prediction from bank dataset with Interpretable Machine Learning
xmlx-io/.github
XMLX GitHub configuration