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Can auto-sklearn deal with highly imbalanced data?
What about integrating a SMOTHE resampling one-class learning strategies?
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Request for project inclusion in scikit-learn-contrib
- Project name: imbalanced-learn (aka imblearn)
- Project description: A python toolbox to tackle the curse of imbalanced datasets in machine lear…
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It would be great to cite that claim.
(To the best of my knowledge, and after carefully thinking about the point, my sense is that the claim is incorrect. Impact of imbalance on performance depends …
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This is a great collection of methods for addressing a common problem. Are you aware of any papers that give a good high-level survey of these kind of methods, with comparisons and/or recommendations…
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Is there any support for multi-class classification problems with imbalanced classes?
In particular I'd like to use `undersample()` with a `ClassifTask` with three classes which doesn't seem to be sup…
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Hi,
This is a question, not an issue, I did not know where to ask.
When doing transfer learning for flower data, [data/download_and_preprocess_flowers.sh](https://github.com/tensorflow/models/blob/…
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I would like to use gunrock for graph clustering and subsequent visualization in Python. Is this possible?
Thank you,
Amir
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See http://scikit-learn.org/stable/auto_examples/document_classification_20newsgroups.html
Classify and automatically label issues.
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Currently, there are only a few unit tests in `tests.py`. These are basic unit tests and don't cover a large portion of the project. We should expand the unit tests to cover more of the core TPOT func…
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Requires a "sum tree" binary heap for efficient execution.
**Edit 2016-06-02:** Please keep to the [contributing guidelines](https://github.com/Kaixhin/Atari/blob/master/CONTRIBUTING.md#using-the-iss…