predict.boosted_regression_forest: Predict with a boosted regression forest.

In grf: Generalized Random Forests

Predict with a boosted regression forest.

Description

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

Usage

## S3 method for class 'boosted_regression_forest'
predict(
  object,
  newdata = NULL,
  boost.predict.steps = NULL,
  num.threads = NULL,
  ...
)

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
boost.predict.steps	Number of boosting iterations to use for prediction. If blank, uses the full number of steps for the object given
num.threads	the number of threads used in prediction
...	Additional arguments (currently ignored).

Value

A vector of predictions.

Examples

# Train a boosted regression forest.
n <- 50
p <- 10
X <- matrix(rnorm(n * p), n, p)
Y <- X[, 1] * rnorm(n)
r.boosted.forest <- boosted_regression_forest(X, Y)

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

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

grf documentation built on June 24, 2024, 5:20 p.m.