features_target_weights.md

[features, target, and weights](@id input)

Methods for extracting certain parts of data for all supported calls of the form fit(learner, data).

LearnAPI.features(learner, data) -> <training "features"; suitable input for `predict` or `transform`>
LearnAPI.target(learner, data) -> <target variable>
LearnAPI.weights(learner, data) -> <per-observation weights>

Here data is something supported in a call of the form fit(learner, data).

Typical workflow

Not typically appearing in a general user's workflow but useful in meta-alagorithms, such as cross-validation (see the example in [obs and Data Interfaces](@ref data_interface)).

Supposing learner is a supervised classifier predicting a vector target:

model = fit(learner, data)
X = LearnAPI.features(learner, data)
y = LearnAPI.target(learner, data)
ŷ = predict(model, Point(), X)
training_loss = sum(ŷ .!= y)

Implementation guide

method	fallback return value	compulsory?
LearnAPI.features(learner, data)	no fallback	no
LearnAPI.target(learner, data)	no fallback	no
LearnAPI.weights(learner, data)	no fallback	no

Reference

LearnAPI.features
LearnAPI.target
LearnAPI.weights