Random forest on the flattened representation

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 or a count head, a count being cut on its variance and a leaf reporting its mean. 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.

Usage

forest(
  data = NULL,
  trees = NULL,
  mtry = NULL,
  min_node = NULL,
  balance = FALSE,
  preset = c("default", "bigboss"),
  seed = 1L,
  threads = 1L
)

Arguments

data: A representation the learner is pinned to, or NULL to run across every representation of the run.
trees: Trees in the forest.
mtry: Columns drawn for each node, the best of which it is split on.
min_node: Units each side of a split keeps.
balance: Whether each tree draws as many units from each class as the smaller holds.
preset: Whose defaults the settings left NULL take: "default" or "bigboss".
seed: Seed for the bootstrap draws and the column draws.
threads: Responses' forests grown at once, or, with one response to fit, its trees. The forest is the same on any number.

Value

A learner().

Details

Each tree is grown on a bootstrap draw of the units, as many draws as there are units, and each node is split on the best of mtry columns drawn for it, by the Gini index or the sum of squares tree() splits by. A tree is grown out: a node is split while it holds at least twice min_node units and its responses differ. A leaf reports the share of presences among the draws it holds, or their mean, and the forest the mean over its trees. The forest is grown by the core the Python package calls, and every draw comes from one generator seeded per tree, so the two languages grow the same forest on any number of threads.

balance = TRUE is the down-sampled forest biomod2 fits as RFd: each tree draws as many units from each class as the smaller class holds, so a rare response's presences are half of every tree's draw.

preset says whose defaults the settings left NULL take. "default" is randomForest's own, which is what biomod2's default option set fits: 500 trees, mtry the square root of the column count under presence-absence and a third of it under a squared-error loss, and min_node 1 and 5 under the two. "bigboss" is biomod2's tuned option set: 500 trees, mtry = 2 and min_node = 5. A setting given explicitly beats either, and an mtry above the column count is the column count.

The case weights are the response head's, positive_weights() under presence-absence, and weight the bootstrap draw: a unit is drawn in proportion to its weight, and within its class under balance. Under the shipped presence-absence head those weights are on, so a default forest() is randomForest's specification fitted under them; a head registered without weights fits it unweighted.

Examples

forest(trees = 200L)
forest(preset = "bigboss", balance = TRUE)