georgian-io-archive / automl_benchmark

Related projects ⓘ

Alternatives and complementary repositories for automl_benchmark

• MI2DataLab / pyBreakDown
  Python implementation of R package breakDown
  ☆41Updated last year
• TomAugspurger / dask-tutorial-odsc-2018
  ☆15Updated 2 years ago
• SauceCat / Nuance
  Visualization ideas for data science
  ☆19Updated 6 years ago
• h2oai / article-information-2019
  Article for Special Edition of Information: Machine Learning with Python
  ☆13Updated 4 years ago
• jphall663 / secure_ML_ideas
  Practical ideas on securing machine learning models
  ☆36Updated 3 years ago
• parsing-science / pydata_nyc_nov_2017
  ☆30Updated 6 years ago
• jphall663 / kdd_2019
  Paper and talk from KDD 2019 XAI Workshop
  ☆20Updated 4 years ago
• h2oai / driverlessai-tutorials
  H2OAI Driverless AI Code Samples and Tutorials
  ☆37Updated 2 weeks ago
• Laurae2 / CategoricalAnalysis
  Analysis of Categorical Encodings for dense Decision Trees
  ☆41Updated 7 years ago
• alteryx / predict-correct-answer
  Predict whether a student will correctly answer a problem based on past performance using automated feature engineering
  ☆32Updated 4 years ago
• klemag / PyconUS_2019-model-interpretability-tutorial
  Content for the Model Interpretability Tutorial at Pycon US 2019
  ☆41Updated 3 months ago
• jphall663 / GWU_rml
  ☆35Updated 9 months ago
• ShopRunner / octopod
  Train multi-task image, text, or ensemble (image + text) models
  ☆45Updated last year
• drivendataorg / power-laws-cold-start
  ☆22Updated last year
• CamDavidsonPilon / PyDataNY_2019_tutorial
  Repo for PyData 2019 Tutorial - New Trends in Estimation and Inference
  ☆26Updated 5 years ago
• springcoil / PyDataLondonTutorial
  PyDataLondonTutorial
  ☆26Updated 8 years ago
• BleckwenAI / mli-catalog
  A catalog of Jupyter Notebooks presenting new techniques to interpret black box machine learning models.
  ☆15Updated 5 years ago
• lopusz / pydata-berlin-2018
  ☆15Updated 6 years ago
• koaning / scikit-fairness
  this repo might get accepted
  ☆29Updated 3 years ago
• minerva-ml / steppy-examples
  Examples and tutorials to the steppy library
  ☆21Updated 6 years ago
• aredier / trelawney
  General Interpretability Package
  ☆58Updated last year
• weehyong / MLScore
  Know your ML Score based on Sculley's paper
  ☆34Updated 5 years ago
• fastforwardlabs / couples-lime
  Notebook demonstrating use of LIME to interpret a model of long-term relationship success
  ☆24Updated 7 years ago
• drivendataorg / drivendata-submission-validator
  Simple validator for submissions to DrivenData competitions
  ☆19Updated 5 years ago
• luke14free / bulldog
  State management framework for Data Science & Analytics
  ☆19Updated 5 years ago
• thomasjpfan / skconfig
  Experimental library for sampling and validating scikit-learn parameters
  ☆10Updated 5 years ago
• ModelOriented / pyCeterisParibus
  Python library for Ceteris Paribus Plots (What-if plots)
  ☆19Updated 3 years ago
• leenamurgai / debias-ml
  A practical, explainable and effective method for reducing bias in machine learning algorithms.
  ☆21Updated 4 years ago
• jphall663 / xai_misconceptions
  Preprint/draft article/blog on some explainable machine learning misconceptions. WIP!
  ☆28Updated 5 years ago