One forest per response, over every bin-by-channel column of the representation: a probability forest under a presence-absence head and a regression forest under a head with a squared-error loss. Trees split on one column at a time and pay nothing for columns that carry nothing, so a forest reads a wide tabular representation without a penalty path and without a selection step, and it finds an interaction between two bins that a linear model would need the product term for.
Arguments
- data
A representation the learner is pinned to, or
NULLto run across every representation of the run.- trees
Trees in the forest.
- mtry
Columns tried at each split, or
NULLfor the square root of the column count.- min_node
Smallest node a split is made on.
- seed
Seed for the bootstrap draw and the split sampling, which are random and would otherwise make the fit irreproducible.
Value
A learner().
Details
The case weights are the response head's, positive_weights() under presence-absence, and
weight the bootstrap draw: a rare response is not fitted away here for a reason the other
learners do not share, because every learner that ships reads the same weights.