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May I ask why, in the multi-segmentation dataset, we assign the class_id to random existing classes?
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- Due to computational restrictions, we only used 10% of our dataset.
- Even our best model does not give best performance
- class imbalance
- too many false positives, need a measure to deal with …
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Hello. Thank you for these loss functions they are very helpful.
I am curious about how to use the constant terms (alpha, beta, gamma, and epsilon) to factor in class imbalances. I am working on a…
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I'm trying to replicate the ICASSP 2022 paper result (A Lightweight Instrument-Agnostic Model for Polyphonic Note Transcription and Multipitch Estimation).
Having some trouble getting the model to…
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### pycaret version checks
- [X] I have checked that this issue has not already been reported [here](https://github.com/pycaret/pycaret/issues).
- [X] I have confirmed this bug exists on the [la…
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Made new dataset due to imbalanced dataset.
For now there are 4 classes of Logistics Sign, which I personally shoot in outdoor, and some of them were crawled in Web
Fragile - 218
Handle - 111
…
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If labels are highly imbalanced (for example, TP53 in ovarian cancer) ROC can break because some cross-validation splits will only have one class.
Maybe using [StratifiedKFold](https://scikit-learn…
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**Is your feature request related to a problem? Please describe.**
I aims to run the lightgbm model for a multiclass classification problem. But I didn't find a feature parameter to balanced the data…
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In the docs, it's frequently mentioned in the references
> Supports multi-class resampling. A one-vs.-rest scheme is used when sampling a class as proposed in [1].
So far, every time I read the r…