A base Julia interface for machine learning and statistics
Comprehensive documentation is here.
New contributions welcome. See the road map.
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)
Created by Anthony Blaom, in cooperation with Cameron Bieganek and other members of the Julia community.