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Once you import a python model using Sckitlearn.jl the documentation is available:
julia>@sk_import linear_model: Lasso
PyObject <class 'sklearn.linear_model._coordinate_descent.Lasso'>
help?> Lasso
search: Lasso LassoRegressor LassoCVRegressor LassoLarsRegressor LassoLarsICRegressor LassoLarsCVRegressor
Linear Model trained with L1 prior as regularizer (aka the Lasso)
The optimization objective for Lasso is::
(1/ (2* n_samples)) *||y - Xw||^2_2+ alpha *||w||_1
...
However, we do not want using MLJScikitLearnInterface to generate every python model to get this documentation, for inclusion in MLJ model docstrings. (Currently, the python objects are only generated at run time, when someone calls fit on the MLJ model; see here). So I guess we generate this documentation separately and have __init__ load it in? Then there is the problem of synchronisation. Can julia artefacts help here?
The text was updated successfully, but these errors were encountered:
Once you import a python model using Sckitlearn.jl the documentation is available:
However, we do not want
using MLJScikitLearnInterface
to generate every python model to get this documentation, for inclusion in MLJ model docstrings. (Currently, the python objects are only generated at run time, when someone callsfit
on the MLJ model; see here). So I guess we generate this documentation separately and have__init__
load it in? Then there is the problem of synchronisation. Can julia artefacts help here?The text was updated successfully, but these errors were encountered: