LearnAPI.jl
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JuliaAI
A Julia interface for training and applying models in machine learning and statistics
This is a substantial revision aimed at simplification, whose most important goal was to provide a cleaner opt-out for the `obs` business. The methods `obsfit`, `obspredict`, `obstransform` were previously introduced...
For fuzzy clustering, for example (assigning multiple labels to a single example)
I guess, this is the right way to do this: ```julia function __init__() global DEFAULT_VERBOSITY = Ref{Int}(1) end ``` plus method to set/inspect?
Caret has this and it is well-liked feature. OpenML has a project to learn the best defaults for common supervised learning models, based on large numbers of data sets.
In the old MLLearn terminology, we have *data containers* (observations can be randomly accessed) and mere *iterators*. Dataloaders (as currently implemented in DataUtils.jl) for example, are only iterators and some...
Comment of @jeremiedb, copied from #10: I think I remain a little confused to the extent to which these term can translate unambiguously to the variety of algos and their...
To support external control of iterative models that can more efficiently compute predictions on an evaluation set internally. See the discussion at https://discourse.julialang.org/t/ann-learnapi-jl-proposal-for-a-basement-level-machine-learning-api/93048/12?u=ablaom