shap
A game theoretic approach to explain the output of any machine learning model. (by shap)
awesome-production-machine-learning
A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning (by EthicalML)
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| shap | awesome-production-machine-learning | |
|---|---|---|
| 38 | 9 | |
| 21,536 | 15,858 | |
| 1.6% | 1.5% | |
| 9.4 | 7.4 | |
| 7 days ago | 9 days ago | |
| Jupyter Notebook | ||
| MIT License | MIT License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
shap
Posts with mentions or reviews of shap.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-12-06.
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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.
awesome-production-machine-learning
Posts with mentions or reviews of awesome-production-machine-learning.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-12-13.
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Exploring Open-Source Alternatives to Landing AI for Robust MLOps
One trove of treasures is the awesome-production-machine-learning repository on GitHub. This curated list provides a multitude of frameworks, libraries, and software designed to facilitate various stages of the ML lifecycle.
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[P] We are building a curated list of open source tooling for data-centric AI workflows, looking for contributions.
There is a cool, gigantic list for MLOps that I can recommend: https://github.com/EthicalML/awesome-production-machine-learning
- Sqldiff: SQLite Database Difference Utility
- [D] What are the best resources to crack M L system design interviews?
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I'm looking for a tool that let's you visualize the models architecture like this. Any idea what it is called?
https://github.com/EthicalML/awesome-production-machine-learning I think you will find most of the tools to visualize the model on this link.
What are some alternatives?
When comparing shap and awesome-production-machine-learning you can also consider the following projects:
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.
anchor - Code for "High-Precision Model-Agnostic Explanations" paper
awesome-jax - JAX - A curated list of resources https://github.com/google/jax
lucid - A collection of infrastructure and tools for research in neural network interpretability.
articulated-animation - Code for Motion Representations for Articulated Animation paper
netron - Visualizer for neural network, deep learning and machine learning models
jellyfish - 🪼 a python library for doing approximate and phonetic matching of strings.
imodels - Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).