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Add Machine Learning Interpretability #1644
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Add awesome-machine-learning-interpretability
unicorn |
For me, its okay. Thanks for contribution! |
Thanks for making an Awesome list! 🙌 It looks like you didn't read the guidelines closely enough. I noticed multiple things that are not followed. Try going through the list point for point to ensure you follow it. I spent a lot of time creating the guidelines so I wouldn't have to comment on common mistakes, and rather spend my time improving Awesome. |
@vinibeloni Please do a better effort when reviewing. This is very far from following the guidelines. |
Hi @matheusgmaia. Here are some pointers for things you might want to address if you want your PR to be merged:
Also, consider sticking to the following format for link items: Link - Description. Often, you just have long descriptive links or links with no description. |
Thanks for the feedback. |
Thanks a lot for this list. I recommend you use awesome-lint. It points to these shortcomings of your draft.
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This is not explicitly in contribution guidelines but under Python section you have links to zip files
Items should link to a project/product pages with some descriptions and docs. If you need to have the above links maybe its better to point to the home page (first link 'Bayesian Case Model' case) https://users.cs.duke.edu/~cynthia/code.html at least it as a reference to an academic paper. |
@matheusgmaia Bump |
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@matheusgmaia You should make a PR to fix the issues, as requested by @jphall663 here: jphall663/awesome-machine-learning-interpretability#23 |
https://github.com/jphall663/awesome-machine-learning-interpretability
A curated list of awesome machine learning interpretability resources.
PRs Reviewed:
PR1643
PR1641
By submitting this pull request I confirm I've read and complied with the below requirements 🖖
Please read it multiple times. I spent a lot of time on these guidelines and most people miss a lot.
Requirements for your pull request
Don't waste my time. Do a good job, adhere to all the guidelines, and be responsive.
You have to review at least 2 other open pull requests.
Try to prioritize unreviewed PRs, but you can also add more comments to reviewed PRs. Go through the below list when reviewing. This requirement is meant to help make the Awesome project self-sustaining. Comment here which PRs you reviewed. You're expected to put a good effort into this and to be thorough. Look at previous PR reviews for inspiration.
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That means 30 days from either the first real commit or when it was open-sourced. Whatever is most recent.
awesome-lint
on your list and fix the reported issues. If there are false-positives or things that cannot/shouldn't be fixed, please report it.Mobile operating system for Apple phones and tablets.
Prototyping interactive UI designs.
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If you have not put in considerable effort into your list, your pull request will be immediately closed.
awesome-name-of-list
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# Awesome Name of List
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as GitHub topics. I encourage you to add more relevant topics.Contents
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. Casing is up to you.Example:
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Node.js
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.You can still use Travis for list linting, but the badge has no value in the readme.
Inspired by awesome-foo
orInspired by the Awesome project
kinda link at the top of the readme. The Awesome badge is enough.Go to the top and read it again.