A base Julia interface for machine learning and statistics
Comprehensive documentation is here.
New contributions welcome. See the road map.
Configure a learning algorithm, and inspect available functionality:
julia> algorithm = Ridge(lambda=0.1)
julia> LearnAPI.functions(algorithm)
(:(LearnAPI.fit), :(LearnAPI.algorithm), :(LearnAPI.minimize), :(LearnAPI.obs),
:(LearnAPI.features), :(LearnAPI.target), :(LearnAPI.predict), :(LearnAPI.coefficients))
Train:
julia> model = fit(algorithm, 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)
Created by Anthony Blaom, in cooperation with Cameron Bieganek and other members of the Julia community.