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name = " MLJModelInterface"
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uuid = " e80e1ace-859a-464e-9ed9-23947d8ae3ea"
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authors = [" Thibaut Lienart and Anthony Blaom" ]
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- version = " 1.9.5 "
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+ version = " 1.9.6 "
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[deps ]
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Random = " 9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
Original file line number Diff line number Diff line change @@ -9,11 +9,11 @@ machine learning models into
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| :-----------: | :------: |
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| [ ![ Build Status] ( https://github.com/JuliaAI/MLJModelInterface.jl/workflows/CI/badge.svg )] ( https://github.com/JuliaAI/MLJModelInterface.jl/actions ) | [ ![ codecov.io] ( http://codecov.io/github/JuliaAI/MLJModelInterface.jl/coverage.svg?branch=master )] ( http://codecov.io/github/JuliaAI/MLJModelInterface.jl?branch=master ) |
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- [ ![ Stable] ( https://img.shields.io/badge/docs-stable-blue.svg )] ( https://juliaai.github.io/MLJModelInterface.jl/stable / )
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+ [ ![ Stable] ( https://img.shields.io/badge/docs-stable-blue.svg )] ( https://juliaai.github.io/MLJModelInterface.jl/dev / )
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[ MLJ] ( https://alan-turing-institute.github.io/MLJ.jl/dev/ ) is a framework for evaluating,
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combining and optimizing machine learning models in Julia. A third party package wanting
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to integrate their machine learning models into MLJ must import the module
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` MLJModelInterface ` defined in this package, as described in the
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- [ documentation] (( https://juliaai.github.io/MLJModelInterface.jl/stable / ).
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+ [ documentation] ( https://juliaai.github.io/MLJModelInterface.jl/dev / ) .
Original file line number Diff line number Diff line change @@ -59,7 +59,7 @@ function supervised_fit_data_scitype(M)
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W = AbstractVector{<: Union{Continuous, Count} } # weight scitype
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return Union{ret, Tuple{I, T, W}}
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elseif supports_class_weights (M)
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- W = AbstractDict{Finite, <: Union{Continuous, Count} }
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+ W = Any
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return Union{ret, Tuple{I, T, W}}
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end
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return ret
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