F1 score metric for classification #186
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lars-reimann
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Closes #187. Closes #186 . ### Summary of Changes Added recall and F1-score functions to the _classifier. Co-authered-by: [email protected] --------- Co-authored-by: Lars Reimann <[email protected]> Co-authored-by: megalinter-bot <[email protected]>
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May 11, 2023
## [0.12.0](v0.11.0...v0.12.0) (2023-05-11) ### Features * add `learning_rate` to AdaBoost classifier and regressor. ([#251](#251)) ([7f74440](7f74440)), closes [#167](#167) * add alpha parameter to `lasso_regression` ([#232](#232)) ([b5050b9](b5050b9)), closes [#163](#163) * add parameter `lasso_ratio` to `ElasticNetRegression` ([#237](#237)) ([4a1a736](4a1a736)), closes [#166](#166) * Add parameter `number_of_tree` to `RandomForest` classifier and regressor ([#230](#230)) ([414336a](414336a)), closes [#161](#161) * Added `Table.plot_boxplots` to plot a boxplot for each numerical column in the table ([#254](#254)) ([0203a0c](0203a0c)), closes [#156](#156) [#239](#239) * Added `Table.plot_histograms` to plot a histogram for each column in the table ([#252](#252)) ([e27d410](e27d410)), closes [#157](#157) * Added `Table.transform_table` method which returns the transformed Table ([#229](#229)) ([0a9ce72](0a9ce72)), closes [#110](#110) * Added alpha parameter to `RidgeRegression` ([#231](#231)) ([1ddc948](1ddc948)), closes [#164](#164) * Added Column#transform ([#270](#270)) ([40fb756](40fb756)), closes [#255](#255) * Added method `Table.inverse_transform_table` which returns the original table ([#227](#227)) ([846bf23](846bf23)), closes [#111](#111) * Added parameter `c` to `SupportVectorMachines` ([#267](#267)) ([a88eb8b](a88eb8b)), closes [#169](#169) * Added parameter `maximum_number_of_learner` and `learner` to `AdaBoost` ([#269](#269)) ([bb5a07e](bb5a07e)), closes [#171](#171) [#173](#173) * Added parameter `number_of_trees` to `GradientBoosting` ([#268](#268)) ([766f2ff](766f2ff)), closes [#170](#170) * Allow arguments of type pathlib.Path for file I/O methods ([#228](#228)) ([2b58c82](2b58c82)), closes [#146](#146) * convert `Schema` to `dict` and format it nicely in a notebook ([#244](#244)) ([ad1cac5](ad1cac5)), closes [#151](#151) * Convert between Excel file and `Table` ([#233](#233)) ([0d7a998](0d7a998)), closes [#138](#138) [#139](#139) * convert containers for tabular data to HTML ([#243](#243)) ([683c279](683c279)), closes [#140](#140) * make `Column` a subclass of `Sequence` ([#245](#245)) ([a35b943](a35b943)) * mark optional hyperparameters as keyword only ([#296](#296)) ([44a41eb](44a41eb)), closes [#278](#278) * move exceptions back to common package ([#295](#295)) ([a91172c](a91172c)), closes [#177](#177) [#262](#262) * precision metric for classification ([#272](#272)) ([5adadad](5adadad)), closes [#185](#185) * Raise error if an untagged table is used instead of a `TaggedTable` ([#234](#234)) ([8eea3dd](8eea3dd)), closes [#192](#192) * recall and F1-score metrics for classification ([#277](#277)) ([2cf93cc](2cf93cc)), closes [#187](#187) [#186](#186) * replace prefix `n` with `number_of` ([#250](#250)) ([f4f44a6](f4f44a6)), closes [#171](#171) * set `alpha` parameter for regularization of `ElasticNetRegression` ([#238](#238)) ([e642d1d](e642d1d)), closes [#165](#165) * Set `column_names` in `fit` methods of table transformers to be required ([#225](#225)) ([2856296](2856296)), closes [#179](#179) * set learning rate of Gradient Boosting models ([#253](#253)) ([9ffaf55](9ffaf55)), closes [#168](#168) * Support vector machine for regression and for classification ([#236](#236)) ([7f6c3bd](7f6c3bd)), closes [#154](#154) * usable constructor for `Table` ([#294](#294)) ([56a1fc4](56a1fc4)), closes [#266](#266) * usable constructor for `TaggedTable` ([#299](#299)) ([01c3ad9](01c3ad9)), closes [#293](#293) ### Bug Fixes * OneHotEncoder no longer creates duplicate column names ([#271](#271)) ([f604666](f604666)), closes [#201](#201) * selectively ignore one warning instead of all warnings ([#235](#235)) ([3aad07d](3aad07d))
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🎉 This issue has been resolved in version 0.12.0 🎉 The release is available on:
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Is your feature request related to a problem?
Computing the$F_1$ score of a classifier for a specific class is a common task.
Desired solution
f1_scoreinClassifierpositive_classto specify which value should be treated as positive (all other classes are treated as negative)sklearn.metrics.f1_scoreFormula:
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