predict.multi_regression_forest: Predict with a multi regression forest

In grf: Generalized Random Forests

Predict with a multi regression forest

Description

Gets estimates of E[Y_i | X = x] using a trained multi regression forest.

Usage

## S3 method for class 'multi_regression_forest'
predict(object, newdata = NULL, num.threads = NULL, drop = FALSE, ...)

Arguments

object	The trained forest.
newdata	Points at which predictions should be made. If NULL, makes out-of-bag predictions on the training set instead (i.e., provides predictions at Xi using only trees that did not use the i-th training example). Note that this matrix should have the number of columns as the training matrix, and that the columns must appear in the same order.
num.threads	Number of threads used in training. If set to NULL, the software automatically selects an appropriate amount.
drop	If TRUE, coerce the prediction result to the lowest possible dimension. Default is FALSE.
...	Additional arguments (currently ignored).

Value

A list containing 'predictions': a matrix of predictions for each outcome.

Examples

# Train a standard regression forest.
n <- 500
p <- 5
X <- matrix(rnorm(n * p), n, p)
Y <-  X[, 1, drop = FALSE] %*% cbind(1, 2) + rnorm(n)
mr.forest <- multi_regression_forest(X, Y)

# Predict using the forest.
X.test <- matrix(0, 101, p)
X.test[, 1] <- seq(-2, 2, length.out = 101)
mr.pred <- predict(mr.forest, X.test)

# Predict on out-of-bag training samples.
mr.pred <- predict(mr.forest)

grf documentation built on Oct. 1, 2023, 1:07 a.m.