grf (version 1.2.0)

predict.custom_forest: Predict with a custom forest.

Description

Predict with a custom forest.

Usage

# S3 method for custom_forest
predict(object, newdata = 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.

num.threads

Number of threads used in training. If set to NULL, the software automatically selects an appropriate amount.

...

Additional arguments (currently ignored).

Value

Vector of predictions.

Examples

# NOT RUN {
# Train a custom forest.
n <- 50
p <- 10
X <- matrix(rnorm(n * p), n, p)
Y <- X[, 1] * rnorm(n)
c.forest <- custom_forest(X, Y)

# Predict using the forest.
X.test <- matrix(0, 101, p)
X.test[, 1] <- seq(-2, 2, length.out = 101)
c.pred <- predict(c.forest, X.test)
# }
# NOT RUN {
# }