Case weights that balance a rare response

The weight every learner that ships fits a presence-absence response under: each presence of a response weighs the ratio of absences to presences among the fitting units, capped, and each absence weighs one. A response with a presence in one target of a hundred is otherwise fitted away by any learner that minimises a mean loss, and the encoders, the penalised fit, the forest and the forward search would each have to decide that for themselves.

Usage

positive_weights(y, cap = 50, fitting = NULL)

Arguments

y

The response matrix, [unit, variable], 0/1.

cap

Ceiling on the weight a presence is given, at least one.

fitting

A logical vector over the rows of y, TRUE for the units the model is fitted on, or NULL for all of them. The ratio is read off those rows alone.

Value

A numeric [unit, variable] matrix of case weights, one per cell of y.

Details

The ratio is read off the units the model is fitted on and the weight applies to every unit handed in. An encoder holds part of its units back as an inner validation set and reads the loss it stops on under the same weights, so the loss that stops the fit is the loss the fit minimises; a unit held back that way is not in the count the ratio is made from.

The weights are the response head's: the shipped presence-absence head carries this function as its weights, and a head registered with weights = function(y, fitting) positive_weights(y, cap = 20, fitting = fitting) weights every learner by that cap instead. A head without weights is fitted unweighted.

Examples

y <- cbind(rare = c(1, 0, 0, 0, 0, 0), common = c(1, 1, 1, 0, 0, 0))
positive_weights(y)
positive_weights(y, fitting = c(TRUE, TRUE, TRUE, TRUE, FALSE, FALSE))