Alternatives and similar repositories for kdd_2019:

Users that are interested in kdd_2019 are comparing it to the libraries listed below

• jphall663 / secure_ML_ideas
  Practical ideas on securing machine learning models
  ☆36Updated 3 years ago
• jphall663 / xai_misconceptions
  Preprint/draft article/blog on some explainable machine learning misconceptions. WIP!
  ☆28Updated 5 years ago
• MI2DataLab / pyBreakDown
  Python implementation of R package breakDown
  ☆42Updated last year
• jphall663 / GWU_rml
  ☆35Updated last week
• koaning / scikit-fairness
  this repo might get accepted
  ☆29Updated 4 years ago
• fastforwardlabs / couples-lime
  Notebook demonstrating use of LIME to interpret a model of long-term relationship success
  ☆24Updated 7 years ago
• Laurae2 / CategoricalAnalysis
  Analysis of Categorical Encodings for dense Decision Trees
  ☆41Updated 7 years ago
• Informationsfabrik / feature-engineering
  ☆14Updated 5 years ago
• david26694 / sktools
  Helpers for scikit learn
  ☆16Updated 2 years ago
• georgian-io-archive / automl_benchmark
  Distributed, large-scale, benchmarking framework for rigorous assessment of automatic machine learning repositories, projects, and librar…
  ☆30Updated 2 years ago
• pcerda / string_categorical_encoders
  Scripts for paper "Encoding high-cardinality string categorical variables"
  ☆24Updated 5 years ago
• pbiecek / xai_stories
  XAI Stories. Case studies for eXplainable Artificial Intelligence
  ☆29Updated 4 years ago
• lopusz / pydata-berlin-2018
  ☆15Updated 6 years ago
• drivendataorg / power-laws-cold-start
  ☆22Updated last year
• jphall663 / responsible_xai
  Guidelines for the responsible use of explainable AI and machine learning.
  ☆17Updated 2 years ago
• ralphhaygood / sklearn-gbmi
  scikit-learn gradient-boosting-model interactions
  ☆25Updated last year
• jphall663 / hc_ml
  Slides, videos and other potentially useful artifacts from various presentations on responsible machine learning.
  ☆22Updated 5 years ago
• dkn22 / embedder
  Embed categorical variables via neural networks.
  ☆59Updated last year
• ShopRunner / octopod
  Train multi-task image, text, or ensemble (image + text) models
  ☆45Updated last year
• tmadl / sklearn-random-rotation-ensembles
  Scikit-learn compatible implementations of the Random Rotation Ensemble idea of (Blaser & Fryzlewicz, 2016)
  ☆43Updated 8 years ago
• vcerqueira / performance_estimation
  Performance estimation for time series forecasting tasks
  ☆39Updated 4 years ago
• klemag / PyconUS_2019-model-interpretability-tutorial
  Content for the Model Interpretability Tutorial at Pycon US 2019
  ☆41Updated 6 months ago
• navdeep-G / interpretable-ml
  Techniques & resources for training interpretable ML models, explaining ML models, and debugging ML models.
  ☆21Updated 2 years ago
• ModelOriented / SAFE
  Surrogate Assisted Feature Extraction
  ☆36Updated 3 years ago
• RaRe-Technologies / talks
  Presentations & notebooks from our talks /workshops/meetups/etc
  ☆24Updated 6 years ago
• luke14free / bulldog
  State management framework for Data Science & Analytics
  ☆19Updated 5 years ago
• amit-sharma / splitdoor-causal-criterion
  A method for estimating causal effects in time-series data. Uses available data to automatically find natural experiments for identifying…
  ☆15Updated 5 years ago
• YuriyGuts / odsc-target-leakage-workshop
  Workshop on Target Leakage in Machine Learning I taught at ODSC Europe 2018 (London) and ODSC East 2019, 2020 (Boston)
  ☆37Updated 4 years ago
• tmadl / sklearn-interpretable-tree
  Simplified tree-based classifier and regressor for interpretable machine learning (scikit-learn compatible)
  ☆47Updated 4 years ago
• koaning / scikit-partial
  Pipeline components that support partial_fit.
  ☆45Updated 7 months ago