OmniXAI
interpret
| OmniXAI | interpret | |
|---|---|---|
| 1 | 6 | |
| 812 | 6,007 | |
| 2.5% | 0.6% | |
| 4.6 | 9.7 | |
| 14 days ago | 6 days ago | |
| Jupyter Notebook | C++ | |
| BSD 3-clause "New" or "Revised" License | MIT License |
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OmniXAI
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Salesforce AI Open-Sources ‘OmniXAI’: A Python-based Machine Learning Library That Provides One-Stop Explainable AI (XAI) Solution To analyze, Debug, And Interprets AI Models
Continue reading | Checkout the paper, article, github, dashboard
interpret
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[D] Alternatives to the shap explainability package
Maybe InterpretML? It's developed and maintained by Microsoft Research and consolidates a lot of different explainability methods.
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What Are the Most Important Statistical Ideas of the Past 50 Years?
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
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[N] Google confirms DeepMind Health Streams project has been killed off
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
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[Discussion] XGBoost is the way.
Also I'd recommend everyone who works with xgboost to give EBM's a try! They perform comparably (except in the case of extreme interactions) but are actually interpretable! https://github.com/interpretml/interpret/ Beside that they since on runtime they're practically a lookup table they're very quick (at the cost of longer training time).
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[D] Generalized Additive Models… with trees?
Open source code by Microsoft: https://github.com/interpretml/interpret (called EBM in this implementation).
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Machine Learning with Medical Data (unbalanced dataset)
If it's not an image, have a go at Microsoft's Explainable Boosting Maching) https://github.com/interpretml/interpret which is not a GBM but a GAM (Gradient Boosting Machine vs Gradient Additive Model). This will also give you explanation via SHAP or LIME values.
What are some alternatives?
DiCE - Generate Diverse Counterfactual Explanations for any machine learning model.
shap - A game theoretic approach to explain the output of any machine learning model.
eli5 - A library for debugging/inspecting machine learning classifiers and explaining their predictions
shapash - 🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
alibi - Algorithms for explaining machine learning models
SegGradCAM - SEG-GRAD-CAM: Interpretable Semantic Segmentation via Gradient-Weighted Class Activation Mapping
imodels - Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).
DALEX - moDel Agnostic Language for Exploration and eXplanation
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!