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Published January 15, 2020 | Version v0.7.0
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alan-turing-institute/MLJ.jl: v0.7.0

Authors/Creators

  • 1. NeSI/Alan Turing Institute
  • 2. Catalyst Lab, Alan Turing Institute
  • 3. @UCL-RITS
  • 4. IIIT Bhubaneswar
  • 5. @evalparse
  • 6. IBM Research
  • 7. GLOBE Institute
  • 8. SecondSpectrum
  • 9. University of Cambridge
  • 10. Queen Mary, University of London
  • 11. The Alan Turing Institute
  • 12. Brown University
  • 13. r2.ca

Description

  • Update to ScientificTypes 0.5.1. This is mainly to improve performance of scitype and coerce in the case of (possibly) missing values and on arrays of Any type. These changes are mildly breaking but won't effect many users. See these releases notes for details

  • Update to MLJBase 0.10.0:

  • Give the partition function a new keyword argument stratify=nothing for specifying a Finite vector on which to base stratified partitioning. Query ?partition for details (#113)

  • Add new methods for generating synthetic data sets: make_blobs, make_moons, make_circles, make_regression (#155)

  • Improve show method for the results of performance evaluations (calling evaluate!, evaluate)

  • Add keyword argument repeats=1 to evaluate!/evaluate for repeated resampling. For example, specifying resampling=CV(nfolds=3, shuffle=true), repeats=2 to generate 6 per_fold performance estimates for aggregation. Query ?evaluate! for details (https://github.com/alan-turing-institute/MLJ.jl/issues/406)

  • In specifying one-dimensional ranges in tuning, unbounded ranges are now allowed. Query ?range and ?iterator for details.

  • improve show method for MLJType objects that "show as constructed" (https://github.com/alan-turing-institute/MLJ.jl/issues/351)

  • Update MLJModels to 0.7.0:

  • (new model) Add the AdaBoostStumpClassifier from DecisionTree

  • Arrange for clustering algorithms to predict categorical elements instead of integers (https://github.com/alan-turing-institute/MLJ.jl/issues/418)

Files

alan-turing-institute/MLJ.jl-v0.7.0.zip

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Additional details

Related works

Is supplement to
https://github.com/alan-turing-institute/MLJ.jl/tree/v0.7.0 (URL)