Regularization strength for logistic (regression) classifier #750
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Closes #750 ### Summary of Changes Added an optional, keyword-only constructor parameter c: float = 1.0 and passed it to the wrapped scikit-learn estimator. <!-- Please provide a summary of changes in this pull request, ensuring all changes are explained. --> --------- Co-authored-by: grefrathc <s23cgref@uni-bonn.de> Co-authored-by: megalinter-bot <129584137+megalinter-bot@users.noreply.github.com> Co-authored-by: Lars Reimann <mail@larsreimann.com>
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## [0.27.0](v0.26.0...v0.27.0) (2024-07-19) ### Features * join ([#870](#870)) ([5764441](5764441)), closes [#745](#745) * activation function for forward layer ([#891](#891)) ([5b5bb3f](5b5bb3f)), closes [#889](#889) * add `ImageDataset.split` ([#846](#846)) ([3878751](3878751)), closes [#831](#831) * add FunctionalTableTransformer ([#901](#901)) ([37905be](37905be)), closes [#858](#858) * add InvalidFitDataError ([#824](#824)) ([487854c](487854c)), closes [#655](#655) * add KNearestNeighborsImputer ([#864](#864)) ([fcdfecf](fcdfecf)), closes [#743](#743) * add moving average plot ([#836](#836)) ([abcf68a](abcf68a)) * add RobustScaler ([#874](#874)) ([62320a3](62320a3)), closes [#650](#650) [#873](#873) * add SequentialTableTransformer ([#893](#893)) ([e93299f](e93299f)), closes [#802](#802) * add temporal operations ([#832](#832)) ([06eab77](06eab77)) * added 'histogram_2d' in TablePlotter ([#903](#903)) ([4e65ba9](4e65ba9)), closes [#869](#869) [#798](#798) * added from_str_to_temporal and continues prediction ([#767](#767)) ([35f468a](35f468a)), closes [#806](#806) [#765](#765) [#740](#740) [#773](#773) * added GRU layer ([#845](#845)) ([d33cb5d](d33cb5d)) * Adds Dropout Layer ([#868](#868)) ([a76f0a1](a76f0a1)), closes [#848](#848) * dark mode for plots ([#911](#911)) ([5447551](5447551)), closes [#798](#798) * easily create a baseline model ([#811](#811)) ([8e1b995](8e1b995)), closes [#710](#710) * get first cell with value other than `None` ([#904](#904)) ([5a0cdb3](5a0cdb3)), closes [#799](#799) * hyperparameter optimization for fnn models ([#897](#897)) ([c1f66e5](c1f66e5)), closes [#861](#861) * implement violin plots ([#900](#900)) ([9f5992a](9f5992a)), closes [#867](#867) * plot decision tree ([#876](#876)) ([d3f81dc](d3f81dc)), closes [#856](#856) * prediction no longer takes a time series dataset only table ([#838](#838)) ([762e5c2](762e5c2)), closes [#837](#837) * raise if `remove_colums` is called with unknown column by default ([#852](#852)) ([8f78163](8f78163)), closes [#807](#807) * regularization strength for logistic classifier ([#866](#866)) ([9f74e92](9f74e92)), closes [#750](#750) * reorders parameters of RangeScaler and makes them keyword-only ([#847](#847)) ([2b82db7](2b82db7)), closes [#809](#809) * replace seaborn with matplotlib for box_plot ([#863](#863)) ([4ef078e](4ef078e)), closes [#805](#805) [#849](#849) * replaced seaborn with matplotlib for correlation_heatmap ([#850](#850)) ([d4680d4](d4680d4)), closes [#800](#800) [#849](#849) ### Bug Fixes * **deps:** bump urllib3 from 2.2.1 to 2.2.2 ([#842](#842)) ([b81bcd6](b81bcd6)), closes [#3122](https://github.com/Safe-DS/Library/issues/3122) [#3363](https://github.com/Safe-DS/Library/issues/3363) [#3122](https://github.com/Safe-DS/Library/issues/3122) [#3363](https://github.com/Safe-DS/Library/issues/3363) [#3406](https://github.com/Safe-DS/Library/issues/3406) [#3398](https://github.com/Safe-DS/Library/issues/3398) [#3399](https://github.com/Safe-DS/Library/issues/3399) [#3396](https://github.com/Safe-DS/Library/issues/3396) [#3394](https://github.com/Safe-DS/Library/issues/3394) [#3391](https://github.com/Safe-DS/Library/issues/3391) [#3316](https://github.com/Safe-DS/Library/issues/3316) [#3387](https://github.com/Safe-DS/Library/issues/3387) [#3386](https://github.com/Safe-DS/Library/issues/3386) * labels of correlation heatmap ([#894](#894)) ([a88a609](a88a609)), closes [#871](#871) * make multi-processing in baseline models more consistent ([#909](#909)) ([fa24560](fa24560)), closes [#907](#907) ### Performance Improvements * improved performance in various methods in `Image` and `ImageList` ([#879](#879)) ([134e7d8](134e7d8))
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Is your feature request related to a problem?
There should be a way to control regularization of the
LogisticClassifier.Desired solution
Add an optional, keyword-only constructor parameter
c: float = 1.0and pass it to the wrapped scikit-learn estimator.Possible alternatives (optional)
I've originally also considered letting the user choose the penalty ("l1"/"l2"/"elasticnet"). However, most solvers only support "l2" anyway. We should rather stick to "l2" and later choose an appropriate solver internally based on the shape of the data for fast convergence (different issue).
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