New version: MLJTuning v0.8.5

[JuliaRegistrator]

• Registering package: MLJTuning
• Repository: https://github.com/JuliaAI/MLJTuning.jl
• Created by: @ablaom
• Version: v0.8.5
• Commit: bac0ac9bdd4036ba4069b328dab914386a7e9975
• Reviewed by: @ablaom
• Reference: Issue for triggering new releases JuliaAI/MLJTuning.jl#59 (comment)
• Description: Hyperparameter optimization algorithms for use in the MLJ machine learning framework
• Release notes:

- Write the `PerformanceEvaluation` objects computed for each model (hyper-parameter set) to the history, or write compact versions of the same (`CompactPeformanceEvaluation` objects) by providing `TunedModel(...)` a new option `compact_history=true`.  The evaluation objects are accessed like this: `evaluation = report(mach).history[index].evaluation`, where `mach` is a machine associated with the `TunedModel` instance. For more on the differences between `PerformanceEvaluation` and `CompactPerformanceEvaluation` objects, refer to their document strings. (In MLJTuning 0.5.3 and 0.5.4 an experimental feature already introduced `PerformanceEvalution` objects to the history, but with no option to write the compact form. In the current release, compact objects are written *by default*.)

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