@@ -48,17 +48,17 @@ It recursively removes features, training a base model on the remaining features
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evaluating their importance until the desired number of features is selected.
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Construct an instance with default hyper-parameters using the syntax
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- `model = RecursiveFeatureElimination(model=...)`. Provide keyword arguments to override
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+ `rfe_model = RecursiveFeatureElimination(model=...)`. Provide keyword arguments to override
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hyper-parameter defaults.
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# Training data
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- In MLJ or MLJBase, bind an instance `model ` to data with
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+ In MLJ or MLJBase, bind an instance `rfe_model ` to data with
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- mach = machine(model , X, y)
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+ mach = machine(rfe_model , X, y)
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OR, if the base model supports weights, as
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- mach = machine(model , X, y, w)
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+ mach = machine(rfe_model , X, y, w)
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Here:
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function MMI. training_losses (model:: RFE , rfe_report)
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return MMI. training_losses (model. model, rfe_report. model_report)
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end
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+
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+ # # Pkg Traits
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+ MMI. metadata_pkg .(
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+ (
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+ DeterministicRecursiveFeatureElimination,
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+ ProbabilisticRecursiveFeatureElimination,
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+ ),
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+ package_name = " FeatureSelection" ,
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+ package_uuid = " 33837fe5-dbff-4c9e-8c2f-c5612fe2b8b6" ,
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+ package_url = " https://github.com/JuliaAI/FeatureSelection.jl" ,
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+ is_pure_julia = true ,
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+ package_license = " MIT"
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+ )
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