POWER-KI
interpret
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POWER-KI
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15 years effort for a Programming Language
To say CIAO there are may ways (see FAST.TRACK or the tutorial)
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 ๐ 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 Modelsโฆ 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?
QUaModbusClient - Modbus to OPC UA Gateway
shap - A game theoretic approach to explain the output of any machine learning model.
AirSim - Open source simulator for autonomous vehicles built on Unreal Engine / Unity, from Microsoft AI & Research
shapash - ๐ Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
carla - Open-source simulator for autonomous driving research.
alibi - Algorithms for explaining machine learning models
open62541pp - C++ wrapper of the open62541 OPC UA library
imodels - Interpretable ML package ๐ for concise, transparent, and accurate predictive modeling (sklearn-compatible).
PyHook - Python hook for ReShade processing
medspacy - Library for clinical NLP with spaCy.
cvui - A (very) simple UI lib built on top of OpenCV drawing primitives
decision-tree-classifier - Decision Tree Classifier and Boosted Random Forest