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[D] Alternatives to the shap explainability package
2 projects | /r/MachineLearning | 25 Nov 2022
Maybe InterpretML? It's developed and maintained by Microsoft Research and consolidates a lot of different explainability methods.
What Are the Most Important Statistical Ideas of the Past 50 Years?
2 projects | news.ycombinator.com | 21 Feb 2022
You may also find Explainable Boosting Machines interesting: https://github.com/interpretml/interpret
They're a bit like a best of both worlds between linear models and random forests (generalized additive models fit with boosted decision trees)
Disclosure: I helped build this open source package
[N] Google confirms DeepMind Health Streams project has been killed off
2 projects | /r/MachineLearning | 1 Sep 2021
Microsoft Explainable Boosting Machine (which is a Gaussian Additive Model and not a Gradient Boosted Trees 🙄 model) is a step in that direction https://github.com/interpretml/interpret
What are some alternatives?
shap - A game theoretic approach to explain the output of any machine learning model.
shapash - 🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
alibi - Algorithms for explaining machine learning models
imodels - Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).
medspacy - Library for clinical NLP with spaCy.
decision-tree-classifier - Decision Tree Classifier and Boosted Random Forest
DashBot-3.0 - Geometry Dash bot to play & finish levels - Now training much faster!
AIF360 - A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.
DALEX - moDel Agnostic Language for Exploration and eXplanation
sagemaker-explaining-credit-decisions - Amazon SageMaker Solution for explaining credit decisions.
yggdrasil-decision-forests - A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models.
DiCE - Generate Diverse Counterfactual Explanations for any machine learning model.