[scikit-learn] imbalanced classes: class_weight

[Christos Aridas]

Hi,

Have you seen http://imbalanced-learn.org?

Best,
Chris

On Tue, 19 Jun 2018 17:53 S Hamidizade, <hamidizade.s at gmail.com> wrote:

> Hi
>
> I would appreciate if you could let me know what is the best way to
> categorize the approaches which have been developed to deal with imbalance
> class problem?
>
> *This article
> <https://www.sciencedirect.com/science/article/pii/S0020025513005124>
> categorizes them into:*
>
>    1. Preprocessing: includes oversampling, undersampling and hybrid
>    methods,
>    2. Cost-sensitive learning: includes direct methods and meta-learning
>    which the latter further divides into thresholding and sampling,
>    3. Ensemble techniques: includes cost-sensitive ensembles and data
>    preprocessing in conjunction with ensemble learning.
>
> *The second <https://dl.acm.org/citation.cfm?id=2907070> classification:*
>
>    1. Data Pre-processing: includes distribution change and weighting the
>    data space. One-class learning is considered as distribution change.
>    2. Special-purpose Learning Methods
>    3. Prediction Post-processing: includes threshold method and
>    cost-sensitive post-processing
>    4. Hybrid Methods:
>
> *The third article
> <https://link.springer.com/article/10.1007/s13748-016-0094-0>:*
>
>    1. Data-level methods
>    2. Algorithm-level methods
>    3. Hybrid methods
>
> The last classification also considers output adjustment as an independent
> approach.
>
> Could you please let me know the class-weight in the sklearn's classifiers
> e.g., logistic regression is classified into which category? Is it true to
> say:
>
> In case of the first categorization, it falls into cost-sensitive learning
>
> In case of the second taxonomy, it would be classified into the third
> category i.e., cost-sensitive post-processing
>
> In case of the third classification, it should fall into algorithm level
>
> Best regards,
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