fix: actually use kernel of support vector machines for training#681
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kernel and degree of support vector machines for trainingkernel of support vector machines for training
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## [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))
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Closes #602
Summary of Changes
Previously, support vector machines always used an RBF kernel, regardless of the
kernelrequested by the user. This is fixed now.