Alternatives and similar repositories for explain-ml

Users that are interested in explain-ml are comparing it to the libraries listed below

• amueller / talks_odt
  Slides and materials for most of my talks by year
  ☆92Updated 2 years ago
• jphall663 / responsible_xai
  Guidelines for the responsible use of explainable AI and machine learning.
  ☆17Updated 2 years ago
• fastforwardlabs / couples-lime
  Notebook demonstrating use of LIME to interpret a model of long-term relationship success
  ☆24Updated 8 years ago
• GaelVaroquaux / interpreting_ml_tuto
  Tutorial on interpreting and understanding machine learning models
  ☆69Updated 6 years ago
• StephenElston / DataScience350
  Material for UW Extension Data Science 350
  ☆19Updated 7 years ago
• rozester / LinkedIn-Comments-Analyzer
  Extracting LinkedIn comments from any post and export it to Excel file
  ☆23Updated 7 years ago
• springcoil / PyDataLondonTutorial
  PyDataLondonTutorial
  ☆26Updated 9 years ago
• jphall663 / diabetes_use_case
  Sample use case for Xavier AI in Healthcare conference: https://www.xavierhealth.org/ai-summit-day2/
  ☆27Updated last year
• drivendataorg / power-laws-cold-start
  ☆24Updated 2 years ago
• yandexdataschool / MLatGradDays
  Course of Machine Learning in Science and Industry at Heidelberg university
  ☆47Updated 8 years ago
• nfmcclure / DataScience350
  Notes for Data Science 350 Class
  ☆24Updated 8 years ago
• fastai / word-embeddings-workshop
  This contains materials for the word embeddings workshop
  ☆127Updated 8 years ago
• mllite / sklearn_explain
  Model explanation provides the ability to interpret the effect of the predictors on the composition of an individual score.
  ☆13Updated 4 years ago
• EricSchles / drifter_ml
  A machine learning testing framework for sklearn and pandas. The goal is to help folks assess whether things have changed over time.
  ☆104Updated 3 years ago
• jphall663 / xai_misconceptions
  Preprint/draft article/blog on some explainable machine learning misconceptions. WIP!
  ☆29Updated 6 years ago
• jphall663 / GWU_rml
  ☆39Updated 5 months ago
• amueller / advanced-sklearn-boston-nlp-2016
  Material and slides for Boston NLP meetup May 23rd 2016
  ☆17Updated 9 years ago
• tmadl / sklearn-interpretable-tree
  Simplified tree-based classifier and regressor for interpretable machine learning (scikit-learn compatible)
  ☆46Updated 4 years ago
• rasbt / DeepLearning-Gdansk2019-tutorial
  Ordinal Regression tutorial for the International Summer School on Deep Learning 2019
  ☆70Updated 6 years ago
• numeristical / introspective
  Repo for the ML_Insights python package
  ☆153Updated 6 months ago
• CamDavidsonPilon / PyDataNY_2019_tutorial
  Repo for PyData 2019 Tutorial - New Trends in Estimation and Inference
  ☆27Updated 5 years ago
• AlexIoannides / pymc-example-project
  Example PyMC3 project for performing Bayesian data analysis using a probabilistic programming approach to machine learning.
  ☆105Updated 6 years ago
• lopusz / pydata-berlin-2018
  ☆15Updated 7 years ago
• dwhitena / dockerized-DS-python
  A simple example of containerized data science with python and Docker.
  ☆51Updated 7 years ago
• jnothman / searchgrid
  Helpers for constructing scikit-learn grid search
  ☆38Updated 5 years ago
• georgian-io-archive / automl_benchmark
  Distributed, large-scale, benchmarking framework for rigorous assessment of automatic machine learning repositories, projects, and librar…
  ☆30Updated 3 years ago
• shivam5992 / aster
  a python based module (bot) to generate kaggle baseline kernels
  ☆26Updated 7 years ago
• thedataincubator / DS30_4
  Brian Farris' Talk on Reinforcement Learning and Multi-Armed Bandits for the Data Incubator
  ☆30Updated 7 years ago
• brohrer / cottonwood
  A flexible neural network framework for running experiments and trying ideas.
  ☆82Updated 5 years ago
• fastai / randomized-SVD
  demos for PyBay talk: Using Randomness to make code faster
  ☆51Updated 8 years ago