Alternatives and similar repositories for fair-ml-reading-group

Users that are interested in fair-ml-reading-group are comparing it to the libraries listed below

• weehyong / MLScore
  Know your ML Score based on Sculley's paper
  ☆34Updated 6 years ago
• navdeep-G / interpretable-ml
  Techniques & resources for training interpretable ML models, explaining ML models, and debugging ML models.
  ☆21Updated 3 years ago
• microsoft / AdversarialGMM
  Minimax Estimation of Conditional Moment Models
  ☆32Updated 2 years ago
• fairlearn / talks
  Talks / presentations / tutorials about Fairlearn and fairness in ML
  ☆22Updated 3 years ago
• MI2DataLab / pyBreakDown
  Python implementation of R package breakDown
  ☆43Updated 2 years ago
• parrt / stratx
  stratx is a library for A Stratification Approach to Partial Dependence for Codependent Variables
  ☆66Updated last year
• EthicalML / explainability-and-bias
  ☆103Updated 2 years ago
• yoshavit / fairml-farm
  A collection of implementations of fair ML algorithms
  ☆12Updated 7 years ago
• nickfrosst / neural_additive_models
  stand alone Neural Additive Models, forked from google-reasearch for easy import to colab
  ☆29Updated 5 years ago
• aschleg / hypothetical
  Hypothesis and statistical testing in Python
  ☆65Updated 5 years ago
• ModelOriented / pyCeterisParibus
  Python library for Ceteris Paribus Plots (What-if plots)
  ☆27Updated 4 years ago
• jphall663 / secure_ML_ideas
  Practical ideas on securing machine learning models
  ☆36Updated 4 years ago
• jphall663 / xai_misconceptions
  Preprint/draft article/blog on some explainable machine learning misconceptions. WIP!
  ☆29Updated 6 years ago
• probml / bandits
  Bayesian Bandits
  ☆68Updated 2 years ago
• firmai / ml-fairness-framework
  FairPut - Machine Learning Fairness Framework with LightGBM — Explainability, Robustness, Fairness (by @firmai)
  ☆72Updated 4 years ago
• wilsonjr / ClusterShapley
  Explaining dimensionality results using SHAP values
  ☆55Updated 10 months ago
• jphall663 / responsible_xai
  Guidelines for the responsible use of explainable AI and machine learning.
  ☆17Updated 2 years ago
• rasbt / DeepLearning-Gdansk2019-tutorial
  Ordinal Regression tutorial for the International Summer School on Deep Learning 2019
  ☆70Updated 6 years ago
• eigenfoo / tests-as-linear
  Python port of "Common statistical tests are linear models" by Jonas Kristoffer Lindeløv.
  ☆95Updated last year
• CamDavidsonPilon / PyDataNY_2019_tutorial
  Repo for PyData 2019 Tutorial - New Trends in Estimation and Inference
  ☆26Updated 6 years ago
• fastai / ethics.fast.ai
  The fast.ai data ethics course
  ☆17Updated 2 years ago
• iancovert / removal-explanations
  A lightweight implementation of removal-based explanations for ML models.
  ☆59Updated 4 years ago
• leenamurgai / debias-ml
  A practical, explainable and effective method for reducing bias in machine learning algorithms.
  ☆22Updated 5 years ago
• lopusz / pydata-berlin-2018
  ☆15Updated 7 years ago
• google-research / sloe-logistic
  ☆29Updated last year
• LeihuaYe / Causal-Inference-Using-Quasi-Experimental-Methods
  Causal Inference Using Quasi-Experimental Methods
  ☆20Updated 4 years ago
• ai-fast-track / fastai-visual-guide
  Notebooks for Fastai Viusal Guide
  ☆25Updated 2 years ago
• yanneta / deep-learning-data-institute
  Repo for the course "Fundamentals of Deep Learning with Pytorch"
  ☆39Updated 4 years ago
• Isaac-Flath / QuartoTemplates
  ☆20Updated 3 years ago
• mkocaoglu / Entropic-Causality
  ☆10Updated 9 years ago