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SVC/SVR kernel and degree are never passed to scikit-learn #602

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lars-reimann opened this issue Apr 1, 2024 · 1 comment · Fixed by #681
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SVC/SVR kernel and degree are never passed to scikit-learn #602

lars-reimann opened this issue Apr 1, 2024 · 1 comment · Fixed by #681
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bug 🪲 Something isn't working released Included in a release

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@lars-reimann
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Describe the bug

This issue affects SupportVectorMachineClassifier and SupportVectorMachineRegressor:

These models never set the kernel of the scikit-learn estimators when fitting. Thus, the default value ("rbf") is always used. Likewise, the degree is not set if a polynomial kernel is chosen.

To Reproduce

Check the code of their _get_sklearn_xy methods.

Expected behavior

The selected kernel should actually be used.

Screenshots (optional)

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Additional Context (optional)

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@lars-reimann lars-reimann added the bug 🪲 Something isn't working label Apr 1, 2024
@github-project-automation github-project-automation bot moved this to Backlog in Library Apr 1, 2024
@lars-reimann lars-reimann changed the title SVC kernel is never passed to scikit-learn SVC/SVR kernel and degree are never passed to scikit-learn` Apr 1, 2024
@lars-reimann lars-reimann changed the title SVC/SVR kernel and degree are never passed to scikit-learn` SVC/SVR kernel and degree are never passed to scikit-learn` Apr 1, 2024
@lars-reimann lars-reimann changed the title SVC/SVR kernel and degree are never passed to scikit-learn` SVC/SVR kernel and degree are never passed to scikit-learn Apr 1, 2024
@lars-reimann lars-reimann self-assigned this May 1, 2024
@lars-reimann lars-reimann moved this from Backlog to Todo in Library May 1, 2024
@lars-reimann lars-reimann moved this from Todo to In Progress in Library May 1, 2024
lars-reimann added a commit that referenced this issue May 1, 2024
)

Closes #602

### Summary of Changes

Previously, support vector machines always used an RBF kernel,
regardless of the `kernel` requested by the user. This is fixed now.
@github-project-automation github-project-automation bot moved this from In Progress to ✔️ Done in Library May 1, 2024
lars-reimann pushed a commit that referenced this issue May 1, 2024
## [0.22.0](v0.21.0...v0.22.0) (2024-05-01)

### Features

* `is_fitted` is now always a property ([#662](#662)) ([b1db881](b1db881)), closes [#586](#586)
* add `Column.missing_value_count` ([#682](#682)) ([f084916](f084916)), closes [#642](#642)
* Add `InputConversion` & `OutputConversion` for nn interface ([#625](#625)) ([fd723f7](fd723f7)), closes [#621](#621)
* Add hash,eq and sizeof in ForwardLayer ([#634](#634)) ([72f7fde](72f7fde)), closes [#633](#633)
* allow using tables that already contain target for prediction ([#687](#687)) ([e9f1cfb](e9f1cfb)), closes [#636](#636)
* callback `Row.sort_columns` takes four parameters instead of two tuples ([#683](#683)) ([9c3e3de](9c3e3de)), closes [#584](#584)
* rename `group_rows_by` in `Table` to `group_rows` ([#661](#661)) ([c1644b7](c1644b7)), closes [#611](#611)
* rename `number_of_column` in `Row` to `number_of_columns` ([#660](#660)) ([0a08296](0a08296)), closes [#646](#646)
* rework `TaggedTable` ([#680](#680)) ([db2b613](db2b613)), closes [#647](#647)
* show missing value count/ratio in summarized statistics ([#684](#684)) ([74b8a35](74b8a35)), closes [#619](#619)
* specify `extras` instead of `features` in `to_tabular_dataset` ([#685](#685)) ([841657f](841657f)), closes [#623](#623)

### Bug Fixes

* actually use `kernel` of support vector machines for training ([#681](#681)) ([09c5082](09c5082)), closes [#602](#602)

### Performance Improvements

* Faster plot_histograms and more reliable plots ([#659](#659)) ([b5f0a12](b5f0a12))
@lars-reimann
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🎉 This issue has been resolved in version 0.22.0 🎉

The release is available on:

Your semantic-release bot 📦🚀

@lars-reimann lars-reimann added the released Included in a release label May 1, 2024
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