learning-machine
shap
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| learning-machine | shap | |
|---|---|---|
| 10 | 38 | |
| 486 | 21,536 | |
| - | 1.6% | |
| 0.0 | 9.4 | |
| 3 months ago | 11 days ago | |
| Python | Jupyter Notebook | |
| Apache License 2.0 | MIT License |
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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.
learning-machine
- Show HN: ML Questions Answered
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Show HN: A Machine Learning Book: Learn ML by Reading Answers, Like So
Hi HN, original poster here!
We made a compilation (book) of questions that we got from 1300+ students from this course [1].
We believe that stackoverflow-like Q/A scheme is best for learning, so we made this.
Project Repo: https://github.com/rentruewang/learning-machine
Website: https://rentruewang.github.io/learning-machine
The website is hosted on GitHub, automatically built from the repo by github actions.
We are lucky to get some feedbacks on reddit here [2], here [3], and here [4], and have made changes accordingly. We really want to know what you guys on HN think. Any suggestions are welcome!
[1] https://speech.ee.ntu.edu.tw/~hylee/ml/2021-spring.html
[2] https://www.reddit.com/r/datascience/comments/oz7xab/open_so...
[3] https://www.reddit.com/r/learnmachinelearning/comments/oz78n...
[4] https://www.reddit.com/r/MachineLearning/comments/oz7p26/p_o...
- Show HN: A Machine Learning Book: Learn ML by Reading Answers, Like SO
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Open Sourced a Machine Learning Book: Learn Machine Learning By Reading Answers, Just Like StackOverflow
[Project Repo](https://github.com/rentruewang/learning-machine)
- Learn machine learning by reading answers to questions, like stack overflow.
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Learn ML by answers to other people's questions!
Website Project Repo.
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Learn machine learning by reading someone else's questions answered.
We are working on a compilation of frequently asked questions! We aim to answer beginner-unfriendly questions in a simple, and strait-forward way so that it will never be asked again. Hope you find this helpful! Website, Project Repo.
- Show HN: A handbook to help students learn machine learning
shap
- Shap v0.45.0
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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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What’s after model adequacy?
We use tools like SHAP to explain what the model is doing to stakeholders.
- Feature importance with feature engineering?
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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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SHAP Value Interpretation
See this closed topic for more detail: https://github.com/slundberg/shap/issues/29
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Christoph Molnar on SHAP Library
Dr. Molnar recently had a semi-viral post on LinkedIn and on Twitter, where he essentially highlights the booming popularity [and power] of using SHAP for explainable AI (which I agree with), but that it also comes with problems; i.e., the open source implementation has thousands of pull requests, bugs, and issues and yet there is no permanent or significant funding to go in and fix them.
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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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Model explainability
txtai pipelines are wrappers around Hugging Face pipelines with logic to easily integrate with txtai's workflow framework. Given that, we can use the SHAP library to explain predictions.
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.
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.
articulated-animation - Code for Motion Representations for Articulated Animation paper
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).
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