Alternatives and similar repositories for interpretable-ml-book

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

• ctgk / PRML
  PRML algorithms implemented in Python
  ☆11,673Updated 8 months ago
• interpretml / interpret
  Fit interpretable models. Explain blackbox machine learning.
  ☆6,728Updated last week
• rasbt / deeplearning-models
  A collection of various deep learning architectures, models, and tips
  ☆17,315Updated last year
• DistrictDataLabs / yellowbrick
  Visual analysis and diagnostic tools to facilitate machine learning model selection.
  ☆4,384Updated 9 months ago
• jrfiedler / causal_inference_python_code
  Python code for part 2 of the book Causal Inference: What If, by Miguel Hernán and James Robins
  ☆1,331Updated 3 years ago
• microsoft / tensorwatch
  Debugging, monitoring and visualization for Python Machine Learning and Data Science
  ☆3,467Updated 2 months ago
• jphall663 / awesome-machine-learning-interpretability
  A curated list of awesome responsible machine learning resources.
  ☆3,934Updated last week
• rasbt / mlxtend
  A library of extension and helper modules for Python's data analysis and machine learning libraries.
  ☆5,084Updated 5 months ago
• graykode / distribution-is-all-you-need
  The basic distribution probability Tutorial for Deep Learning Researchers
  ☆1,641Updated 5 years ago
• marcotcr / lime
  Lime: Explaining the predictions of any machine learning classifier
  ☆12,069Updated last year
• hadrienj / deepLearningBook-Notes
  Notes on the Deep Learning book from Ian Goodfellow, Yoshua Bengio and Aaron Courville (2016)
  ☆1,784Updated 5 years ago
• reiinakano / scikit-plot
  An intuitive library to add plotting functionality to scikit-learn objects.
  ☆2,439Updated last year
• scikit-learn-contrib / imbalanced-learn
  A Python Package to Tackle the Curse of Imbalanced Datasets in Machine Learning
  ☆7,068Updated 3 months ago
• avehtari / BDA_course_Aalto
  Bayesian Data Analysis course at Aalto
  ☆2,244Updated 2 weeks ago
• glouppe / info8010-deep-learning
  Lectures for INFO8010 Deep Learning, ULiège
  ☆1,271Updated 6 months ago
• lopusz / awesome-interpretable-machine-learning
  ☆919Updated 2 years ago
• shap / shap
  A game theoretic approach to explain the output of any machine learning model.
  ☆24,806Updated last week
• parrt / dtreeviz
  A python library for decision tree visualization and model interpretation.
  ☆3,108Updated last week
• ModelOriented / DALEX
  moDel Agnostic Language for Exploration and eXplanation (JMLR 2018; JMLR 2021)
  ☆1,451Updated last month
• Atcold / NYU-DLSP20
  NYU Deep Learning Spring 2020
  ☆6,779Updated 5 months ago
• MingchaoZhu / InterpretableMLBook
  《可解释的机器学习--黑盒模型可解释性理解指南》，该书为《Interpretable Machine Learning》中文版
  ☆4,905Updated 2 years ago
• benedekrozemberczki / awesome-decision-tree-papers
  A collection of research papers on decision, classification and regression trees with implementations.
  ☆2,436Updated last year
• facebookresearch / hiplot
  HiPlot makes understanding high dimensional data easy
  ☆2,802Updated last year
• DeepGraphLearning / LiteratureDL4Graph
  A comprehensive collection of recent papers on graph deep learning
  ☆3,085Updated 4 years ago
• probml / pml-book
  "Probabilistic Machine Learning" - a book series by Kevin Murphy
  ☆5,433Updated 7 months ago
• TeamHG-Memex / eli5
  A library for debugging/inspecting machine learning classifiers and explaining their predictions
  ☆2,773Updated 7 months ago
• dataflowr / notebooks
  code for deep learning courses
  ☆1,146Updated 3 months ago
• krasserm / bayesian-machine-learning
  Notebooks about Bayesian methods for machine learning
  ☆1,902Updated last year
• vahidk / EffectivePyTorch
  PyTorch tutorials and best practices.
  ☆1,702Updated 8 months ago
• probml / pyprobml
  Python code for "Probabilistic Machine learning" book by Kevin Murphy
  ☆6,956Updated last week