grf (version 2.3.2)

predict.boosted_regression_forest: Predict with a boosted regression forest.

Description

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

Usage

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

Value

A vector of predictions.

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).

Examples

# \donttest{
# 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)
# }