Density Estimation

In density estimators, fit is trained only on target data, and predict consumes no data at all, a pattern flagged by the identities LearnAPI.target(learner, y)== y and LearnAPI.kind_of(learner)==LearnAPI.Generative(), respetively.

Typically predict returns a single probability density/mass function. Here's a sample workflow:

model = fit(learner, y) # no features
predict(model)          # shortcut for  `predict(model, Distribution())`, or similar

A one-line convenience method will typically be implemented as well:

predict(learner, y)

However, having the multi-line workflow enables the possibility of updating the model with new data. See this example from the JuliaTestAPI.jl test suite:

• normal distribution estimator