A response head says what the values being predicted are, how they reach a learner, and which cells of the (variable, fold) grid a score is defined on. Presence-absence with a joint multi-label head is what ships; an abundance or phenology response is a registration rather than a second fitting path.
Arguments
- name
Name the response is asked for by.
- spec
A list with elements
prepare(y), returning the numeric matrix a learner is fitted on;activation, the name of the output transform ("sigmoid","identity"or"exp");loss, the name of the training objective ("binary_cross_entropy","squared_error"or"poisson_deviance");metric, the default metric name; andcells(y, folds), returning the mask of scorable cells. Every learner that ships readslossandactivationfrom here: the encoders train under the loss and predict through the activation, and the learners fitting one model per response take the family the loss names, logistic, Gaussian or Poisson. The combiner minimises the same loss. An optionalweights(y, fitting)returns a[unit, variable]matrix of case weights every learner fits under, withfittinga logical vector marking the rows the model is fitted on: whatever the head reads off the response, it reads off those rows, and it weights every row, so an encoder's inner validation loss is weighted as its fit is. The shipped head's ispositive_weights(), and a head without one fits unweighted.- overwrite
Replace an existing registration.