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So now we have data, data interfaces and feature extractor and a naive bayes demo, we need to figure out the problem of unbalanced data. Any clues on how to proceed with this?
I've tried shogun ML s…
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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…
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**Describe the bug**
In Windows 10, under a **new** conda env, simba fails to start with:
```
(...)
ImportError: cannot import name '_OneToOneFeatureMixin'
```
**To Reproduce**
Steps to repro…
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### Use case
common
### Name of resource
SMOTE dataset balancing
### ID
SMOTE_dataset_balancing
### Description
Dataset balancing using SMOTE oversampling technique. A balanced da…
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Will it possible for you to add a description the model you're using? Especially which cost function you used for multi-label classification. If possible, please also provide some accuracy measure on …
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Hi,
I see that `edge_splitter_train.train_test_split()` samples the same number of positive and negative edges.
Currently, I don't see a way to control the ratio of positive and negative edges. I…
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Imbalanced datasets, where the classes have very different occurrence rates, can show up in large data sets.
There are many strategies for dealing with imbalanced data. http://contrib.scikit-learn.…
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Is there any interest to implement some supervised discretization algorithms? I would imagine that it might work better than quantile on kmeans discretization in `KBinsDiscretizer`, particularly on so…