Anthony Blaom, PhD
Anthony Blaom, PhD
@fipelle When [this PR](https://github.com/JuliaAI/MLJDecisionTreeInterface.jl/pull/42) merges you will be able to (efficiently) control early stopping (and [more](https://alan-turing-institute.github.io/MLJ.jl/dev/controlling_iterative_models/#Controlling-Iterative-Models)) through the MLJ interface. A RandomForestClassifier example is given in the PR. Another example...
@salbert83 Any chance you would have time and inclination to take this on?
It would be good to have a more generic implementation. However, it seems that a refactor may be a little more involved. See, for example: https://github.com/JuliaAI/DecisionTree.jl/blob/605e4d41deaa547462f71b5bd05a9a16ad682b15/src/regression/tree.jl#L16
I'd support including your measures at MLJBase/src/measures.jl . BTW, the intention is to make measures are stand-alone package.
> What is the best way to report feature importance @lalvim Perhaps this is relevant: https://github.com/alan-turing-institute/MLJ.jl/issues/747
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@JuliaRegistrator register release notes: - extend compat for MLJModelInterface to include v1.0
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