eli5
lime
| eli5 | lime | |
|---|---|---|
| 1 | 14 | |
| 2,730 | 11,300 | |
| 0.8% | - | |
| 0.0 | 0.0 | |
| about 2 years ago | 18 days ago | |
| Jupyter Notebook | JavaScript | |
| MIT License | BSD 2-clause "Simplified" License |
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eli5
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How to extract keywords important to a text classification problem?
https://github.com/TeamHG-Memex/eli5 can help you.
lime
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Ethical and Bias Testing in Generative AI: A Practical Guide to Ensuring Ethical Conduct with Test Cases and Tools
Other tools like Fairness Indicators, Lime, and SHAP are also valuable resources for ethical and bias testing.
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Government sets out 'adaptable' regulation for AI
A basic form that's useful, it's quite easy. I've used LIME a lot https://github.com/marcotcr/lime
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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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[P] Understanding LIME | Explainable AI
This is a nice brief introduction. Where you could improve is showing how each part of the presentation is mapped to code, so people can play around with it. My advice would be to link to the lime tutorials and fill in any gaps with notebooks of your own. If you can direct your viewers to be practice what you explain and also have safety nets where you explain common problems and solutions, you can differentiate your content from the dozens of other content creators explaining the same tools and concepts you are.
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The cause of a decision in Swahili social media sentiments
In today's article, I will work with you through building a machine learning model for Swahili social media sentiment classification with the interpretability of each prediction of our final model using Local Interpretable Model-Agnostic Explanations.
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"We need to take a pause," research scientist and physician Leo Anthony Celi from the Massachusetts Institute of Technology told the Boston Globe after learning that AI can predict people's race from X-ray images - Science Alert
There is a lot of tools out there that can assist them with that. Lime for example.
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What are some cool error analysis tricks you've seen?
I'm a big fan of LIME https://github.com/marcotcr/lime and sampling random errors.
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Cause of overfitting using vgg16 transfer learning
Or you could see what activates miss-classified labels (e.g. with LIME https://github.com/marcotcr/lime) and try to understand if there are some common causes (e.g. reflection, different lighting, background etc.).
- [Q] What's the community's opinion of "interpretable ML/AI"?
- GitHub - marcotcr/lime: Lime: Explaining the predictions of any machine learning classifier {Python}
What are some alternatives?
linear-tree - A python library to build Model Trees with Linear Models at the leaves.
shap - A game theoretic approach to explain the output of any machine learning model.
ML-Workspace - 🛠All-in-one web-based IDE specialized for machine learning and data science.
anchor - Code for "High-Precision Model-Agnostic Explanations" paper
course-nlp - A Code-First Introduction to NLP course
Fruit-Images-Dataset - Fruits-360: A dataset of images containing fruits and vegetables
graph_summarizer - summarize text using graphs and language vector models
shap - A game theoretic approach to explain the output of any machine learning model. [Moved to: https://github.com/shap/shap]
CodeSearchNet - Datasets, tools, and benchmarks for representation learning of code.
Cause-of-decision-in-Swahili-sentiments - This repository special to demonstrate the cause of decision or explainability on classifying Swahili sentiments as a data professional for business needs.
mlforecast - Scalable machine 🤖 learning for time series forecasting.
fraud-detection-using-machine-learning - Setup end to end demo architecture for predicting fraud events with Machine Learning using Amazon SageMaker