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v0.22.0 source code.tar.gz (6.3 MB)
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| Name | Modified | Size | Downloads / Week |
|---|---|---|---|
| Parent folder | |||
| README.md | 2025-09-10 | 1.6 kB | |
| v0.21.0 source code.tar.gz | 2025-09-10 | 5.2 MB | |
| v0.21.0 source code.zip | 2025-09-10 | 5.2 MB | |
| Totals: 3 Items | 10.4 MB | 0 | |
MLJ v0.21.0
- (new models) Add the following models from MLJTransforms.jl and make them immediately available to the MLJ user (she does not need to use
@loadto load them):OrdinalEncoder,FrequencyEncoder,TargetEncoder,ContrastEncoder,CardinalityReducer,MissingnessEncoder. - (mildly breaking) Have MLJTransforms.jl, instead of MLJModels.jl, provide the following built-in models, whose behaviour is unchanged:
ContinuousEncoder,FillImputer,InteractionTransformer,OneHotEncoder,Standardizer,UnivariateBoxCoxTransformer,UnivariateDiscretizer,UnivariateFillImputer,UnivariateTimeTypeToContinuous,Standardizer.
Guide for possible source of breakage: While it was never necessary to use @load to load one of the models in the last list (assuming you have first run using MLJ) this is frequently not realised by users, and one sees things like @load OneHotEncoder pkg=MLJModels, which this release will break. If such a call is preceded by using MLJ or using MLJTransforms you can remove the loading command altogether (OneHotEncoder() already works), and in any case you can instead use @load OneHotEncoder pkg=MLJTransforms.
Merged pull requests: - Make updates to reflect code reorganisation around addition of MLJTransforms.jl (#1177) (@ablaom) - For a 0.21 release (#1180) (@ablaom)
Closed issues: - Decision trees from ScikitLearn.jl not available (#545) - Document RecursiveFeatureElimination (#1162)
