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A Julia interface for training and applying models in machine learning and statistics

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JuliaAI/LearnAPI.jl

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LearnAPI.jl

A base Julia interface for machine learning and statistics

Lifecycle:Maturing Build Status codecov Docs

Comprehensive documentation is here.

New contributions welcome. See the road map.

Code snippet

Configure a machine learning algorithm:

julia> ridge = Ridge(lambda=0.1)

Inspect available functionality:

julia> @functions ridge
(fit, LearnAPI.learner, LearnAPI.strip, obs, LearnAPI.features, LearnAPI.target, predict, LearnAPI.coefficients

Train:

julia> model = fit(ridge, data)

Predict:

julia> predict(model, data)[1]
"virginica"

Predict a probability distribution (proxy for the target):

julia> predict(model, Distribution(), data)[1]
UnivariateFinite{Multiclass{3}}(setosa=>0.0, versicolor=>0.25, virginica=>0.75)

Credits

Created by Anthony Blaom, in cooperation with Cameron Bieganek and other members of the Julia community.

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A Julia interface for training and applying models in machine learning and statistics

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