articulated-animation
shap
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articulated-animation | shap | |
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4 | 38 | |
1,165 | 21,389 | |
2.4% | 1.6% | |
3.7 | 9.4 | |
26 days ago | 5 days ago | |
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GNU General Public License v3.0 or later | MIT License |
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articulated-animation
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✨ Best Computer Vision Projects with Source Code 🚀
🔗 https://github.com/snap-research/articulated-animation
shap
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[D] Convert a ML model into a rule based system
something like GitHub - shap/shap: A game theoretic approach to explain the output of any machine learning model.?
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[P] tinyshap: A minimal implementation of the SHAP algorithm
A less than 100 lines of code implementation of KernelSHAP because I had a hard time understanding shap's code.
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Model interpretation with many features
https://github.com/slundberg/shap this or https://github.com/marcotcr/lime would be relevant to you, especially if you want to look at explaining a single prediction.
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Random Forest Estimation Question
Option 4) create SHAP values https://github.com/slundberg/shap to better understand what the RF did.
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What Are the Most Important Statistical Ideas of the Past 50 Years?
Seconding Chris Molnar's excellent writeup. I also find the readme & example notebooks in Scott Lundberg's github repo to be a great way to get started. There are also references there for the original papers, which are surprisingly readable, imo. https://github.com/slundberg/shap
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[Q] What's the community's opinion of "interpretable ML/AI"?
I've become a zealot about parametric stats, specifically from the Bayesian paradigm. Something about studying the core business problem, choosing the best distribution(s), and making inferences has been really rewarding for me. But increasingly, I'm seeing tools like SHAP, which allegedly enable users of black-box ML models to intuit what/how their models "think". (SHAP is just one example.)
- Looking into the "black box" of a neural network
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Comparing Strings (Street Names) With Machine Learning
As more features are added to a model, the longer it will take to make a prediction. To help you find a suitable set of features, I have two suggestions, (1) recursive feature selection and (2) SHAP values. Using either of these methods can save you time as you find the right set of features for your model.
- [D] Has anyone ever used the SHAP and LIME models in machine learning?
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How an ML algorithm shows which aspect of a comparison contributes more to the result?
If you are using more of a black-box method, two of the more common ways to determine how your dependent variables interact with your dependent variables are Shapley values and LIME. Shapley values are related to game theory from economics. Basically it attempts to answer how much each feature contributes to the predicted value compared to the average by looking at the average marginal contribution of a specific feature value across all potential combinations of feature values. A good python implementation and more details can be found here.
What are some alternatives?
shapash - 🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
Transformer-Explainability - [CVPR 2021] Official PyTorch implementation for Transformer Interpretability Beyond Attention Visualization, a novel method to visualize classifications by Transformer based networks.
captum - Model interpretability and understanding for PyTorch
lime - Lime: Explaining the predictions of any machine learning classifier
interpret - Fit interpretable models. Explain blackbox machine learning.
first-order-model - This repository contains the source code for the paper First Order Motion Model for Image Animation
Thin-Plate-Spline-Motion-Model - [CVPR 2022] Thin-Plate Spline Motion Model for Image Animation.
awesome-production-machine-learning - A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
anchor - Code for "High-Precision Model-Agnostic Explanations" paper
lucid - A collection of infrastructure and tools for research in neural network interpretability.
jellyfish - 🪼 a python library for doing approximate and phonetic matching of strings.
xbyak - a JIT assembler for x86(IA-32)/x64(AMD64, x86-64) MMX/SSE/SSE2/SSE3/SSSE3/SSE4/FPU/AVX/AVX2/AVX-512 by C++ header