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Releases: JuliaAI/MLJBase.jl

v1.7.0

19 Jul 02:14
4e8c087
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MLJBase v1.7.0

Diff since v1.6.0

Merged pull requests:

  • Relax restrictions on model type in resampling (evaluate!) (#985) (@ablaom)
  • For a 1.7 release (#988) (@ablaom)

v1.6.0

02 Jul 22:30
0849be7
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MLJBase v1.6.0

Diff since v1.5.0

  • (enhancment) Arrange that pipelines support transformers that need a target variable for training (#984)

Merged pull requests:

v1.5.0

24 Jun 23:25
5739a73
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MLJBase v1.5.0

Diff since v1.4.0

  • (enhancement) Add facility to specify a logger globally, with a setter default_logger(logger), and a getter default_logger() (#979)

Merged pull requests:

v1.4.0

03 Jun 08:18
ffe0ac2
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MLJBase v1.4.0

Diff since v1.3.0

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v1.3.0

06 May 02:08
d5f3398
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MLJBase v1.3.0

Diff since v1.2.1

  • (Performance enhancement) Remove type instability for predict(mach::Machine, ...) in the easy and typical case that mach does not wrap a Symbol model (#969)

  • (New feature) Give evaluate and evaluate! the option compact=true, to return a CompactPerformanceEvaluation object with minimal memory footprint (#973)

  • (New feature) Add an InSample() resampling strategy that trains and tests on the same data (whatever is specified by rows, or all supplied data) (#975)

  • (Display improvement) Split the table displayed as part of an PerformanceEvaluation object over two tables, if needed, to deal with overly wide tables (#973)

Merged pull requests:

  • Add prompt to docstring REPL example (#968) (@abhro)
  • Address some predict/transform type instabilities (#969) (@ablaom)
  • Update docstring examples and code (#970) (@abhro)
  • Make test of iterator(...) more robust (#972) (@ablaom)
  • Add CompactPerformanceEvaluation objects and the option in evaluate! to construct them (#973) (@ablaom)
  • Add InSample resampling strategy (#975) (@ablaom)
  • For a 1.3 release (#977) (@ablaom)

Closed issues:

  • Add InSample resampling strategy (#967)
  • Tests failing on dev (#971)
  • selectrows struggling with views of a table (#974)

v1.2.1

17 Mar 23:36
5046989
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MLJBase v1.2.1

Diff since v1.2.0

Merged pull requests:

v1.2.0

17 Mar 19:44
0725e90
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MLJBase v1.2.0

Diff since v1.1.2

  • (enhancement) Expose feature_importances in pipelines with a supporting supervised component, and in TransformedTargetModels with supporting atomic model (#963)

Merged pull requests:

  • Fix some error message diagnostics (#962) (@ablaom)
  • Allow Pipeline and TransformedTargetModel to support feature importances (#963) (@ablaom)
  • For a 1.2.0 release (#964) (@ablaom)

v1.1.2

01 Mar 01:50
30687fb
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MLJBase v1.1.2

Diff since v1.1.1

Merged pull requests:

Closed issues:

  • Serialized Composite Model Fails with XGBoost (#927)
  • Reduce sigdigits in parameter range display (#948)

v1.1.1

23 Jan 10:08
3ef1725
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MLJBase v1.1.1

Diff since v1.1.0

Merged pull requests:

Closed issues:

  • Update the class imbalance POC (#887)
  • Post issues pointing to MLJBase 1.0 migration guide (#937)
  • strategy or example for doing a stratified k-fold (#950)

v1.1.0

10 Jan 00:00
2788980
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MLJBase v1.1.0

Diff since v1.0.1

  • Arrange that calling plot(mach) on a machine mach calls plot(mach.fitresult), allowing model implementations to define plot recipes locally and have them work on machines wrapping their models (#951)

Merged pull requests:

  • Remove measures from docs (#945) (@ablaom)
  • CompatHelper: add new compat entry for Statistics at version 1, (keep existing compat) (#949) (@github-actions[bot])
  • Forward (::Machine).fitresult to RecipesBase (#951) (@MilesCranmer)
  • bump 1.1.0 (#952) (@ablaom)
  • For a 1.1.0 release (#953) (@ablaom)

Closed issues:

  • Measures page in documentation is empty (#944)