nestedcvtraining
cleanlab
| nestedcvtraining | cleanlab | |
|---|---|---|
| 6 | 5 | |
| 27 | 2,254 | |
| - | - | |
| 0.0 | 8.4 | |
| over 1 year ago | almost 3 years ago | |
| Python | Python | |
| MIT License | GNU Affero General Public License v3.0 |
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nestedcvtraining
- [P] Nested Cross Validation Library
- Project: Nested Cross Validation Library
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[D] Andrew Ng's data-centric vs model-centric Machine Learning
Once you have your pipeline, model included, with all the transformers defined and parametrized, you could use an optimizing approach like the one in the examples of this library: https://github.com/JaimeArboleda/nestedcvtraining Do you think it will be a good idea? Or am I oversimplifying?
- [D] What’s the simplest, most lightweight but complete and 100% open source MLOps toolkit?
- [P] New library for performing nested cross validation, optimizing, calibrating and reporting quality of binary classification models
cleanlab
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[P] Confident Learning making ML QA 34x cheaper
Code for https://arxiv.org/abs/1911.00068 found: https://github.com/cgnorthcutt/cleanlab
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Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks
Code: https://github.com/cgnorthcutt/cleanlab
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[D] Andrew Ng's data-centric vs model-centric Machine Learning
I am an author on this, so I am biased. Around half a decade ago, we began developing a field at MIT called confident learning [ paper | blog | reddit post ] that takes a data-centric approach: instead of improving the model quality, it improves the data label quality. It's used by Google, Facebook, and is open-sourced in Python as the cleanlab package.
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[R] Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks
👍An easy first step to find label errors in datasets is cleanlab: https://github.com/cgnorthcutt/cleanlab
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