lime
Lime: Explaining the predictions of any machine learning classifier (by marcotcr)
anchor
Code for "High-Precision Model-Agnostic Explanations" paper (by marcotcr)
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| lime | anchor | |
|---|---|---|
| 14 | 1 | |
| 11,278 | 781 | |
| - | - | |
| 0.0 | 2.6 | |
| 14 days ago | almost 2 years ago | |
| JavaScript | Jupyter Notebook | |
| BSD 2-clause "Simplified" License | BSD 2-clause "Simplified" 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.
lime
Posts with mentions or reviews of lime.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-09-18.
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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}
anchor
Posts with mentions or reviews of anchor.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-01-14.
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[Q] What's the community's opinion of "interpretable ML/AI"?
LIME (https://github.com/marcotcr/lime) and Anchor (https://github.com/marcotcr/anchor), both by Marco Tulio Ribeiro (https://homes.cs.washington.edu/~marcotcr/).
What are some alternatives?
When comparing lime and anchor you can also consider the following projects:
shap - A game theoretic approach to explain the output of any machine learning model.
eli5 - A library for debugging/inspecting machine learning classifiers and explaining their predictions
Fruit-Images-Dataset - Fruits-360: A dataset of images containing fruits and vegetables
shap - A game theoretic approach to explain the output of any machine learning model. [Moved to: https://github.com/shap/shap]
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.