shap
jellyfish
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shap | jellyfish | |
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38 | 3 | |
21,580 | 1,989 | |
1.8% | - | |
9.4 | 6.9 | |
6 days ago | 27 days ago | |
Jupyter Notebook | Jupyter Notebook | |
MIT License | MIT License |
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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.
jellyfish
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Python Libraries
For sounds something like https://github.com/jamesturk/jellyfish ?
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Comparing Strings (Street Names) With Machine Learning
When comparing strings (in our case street names), there are plenty of off-the-shelf features that can be used, such as those provided by the jellyfish. This package also provides a number of phonetic encodings. We can combine an encoding with a metric, such as Levenshtein Distance, to measure the phonetic similarity between two street names.
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How to match names which differ slightly?
You can use a library like jellyfish which implements a bunch of string comparison algorithms, you'd just have to experiment and see which one gives the best results for you. I think I've had the best luck with Jaro-Winkler, then looking at the % match result and picking a cutoff above which I have good confidence that the match is real. It's still not perfect, and I really don't see how your last example would work with just about any automated comparison.
What are some alternatives?
shapash - 🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
fuzzywuzzy - Fuzzy String Matching in Python
Transformer-Explainability - [CVPR 2021] Official PyTorch implementation for Transformer Interpretability Beyond Attention Visualization, a novel method to visualize classifications by Transformer based networks.
TextDistance - 📐 Compute distance between sequences. 30+ algorithms, pure python implementation, common interface, optional external libs usage.
captum - Model interpretability and understanding for PyTorch
Levenshtein - The Levenshtein Python C extension module contains functions for fast computation of Levenshtein distance and string similarity
lime - Lime: Explaining the predictions of any machine learning classifier
Pygments
interpret - Fit interpretable models. Explain blackbox machine learning.
ceja - PySpark phonetic and string matching algorithms
awesome-production-machine-learning - A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
汉字拼音转换工具(Python 版) - 汉字转拼音(pypinyin)