v0.20.0

MLJBase v0.20.0

Diff since v0.19.8

• Relax and simplify scitype checks when constructing machines. The existing fit_data_scitype model trait encodes all allowed fit "scitype" signatures, and scitype checks now only consider this trait. In particular, an appropriately implemented transformer can now be passed a training target without tripping the type checker. (#699, #732) @pazzo83 @ablaom
• (enhancement, breaking) Redesign the serialization API to: (i) Allow use of arbitrary serialization packages for core serialization; (ii) Ensure serialization plays nicely with model composition and meta-algorithms like tuning; (iii) Ensure all traces of training data are absent in serialised models (not previously true for all composite models or if cache=true in machine constructor). Models with non-persistent learned parameters (fitresult) implement a modified model API that is documented here. The new user workflow will shortly appear in the MLJ manual under "Machines". (JuliaAI/MLJSerialization.jl#15, #733, JuliaAI/MLJSerialization.jl#16) @olivierlabayle

Closed issues:

• Relax any checks that block transformers needing to see target in training. (#699)

Merged pull requests:

• Serialization (#733) (@olivierlabayle)
• Add suggestion to err_incompatible_prediction_types message (#748) (@ablaom)
• Add a brief reminder that the fields of the PerformanceEvaluation struct are part of the public API (#749) (@DilumAluthge)
• For a 0.20 release (#751) (@ablaom)
• Fix a problem with confmat and CategoricalValue eltype (#752) (@ablaom)
• For a 0.20 release (#754) (@ablaom)
• Bump version. (#755) (@ablaom)