Predict with a regression forest

Source: R/regression_forest.R

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

# S3 method for regression_forest
predict(
  object,
  newdata = NULL,
  linear.correction.variables = NULL,
  ll.lambda = NULL,
  ll.weight.penalty = FALSE,
  num.threads = NULL,
  estimate.variance = 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.
linear.correction.variables

Optional subset of indexes for variables to be used in local linear prediction. If NULL, standard GRF prediction is used. Otherwise, we run a locally weighted linear regression on the included variables. Please note that this is a beta feature still in development, and may slow down prediction considerably. Defaults to NULL.
ll.lambda

Ridge penalty for local linear predictions. Defaults to NULL and will be cross-validated.
ll.weight.penalty

Option to standardize ridge penalty by covariance (TRUE), or penalize all covariates equally (FALSE). Defaults to FALSE.
num.threads

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

Whether variance estimates for \(\hat\tau(x)\) are desired (for confidence intervals).
...

Additional arguments (currently ignored).

Value

Vector of predictions, along with estimates of the error and (optionally) its variance estimates. Column 'predictions' contains estimates of E[Y|X=x]. The square-root of column 'variance.estimates' is the standard error the test mean-squared error. Column 'excess.error' contains jackknife estimates of the Monte-carlo error. The sum of 'debiased.error' and 'excess.error' is the raw error attained by the current forest, and 'debiased.error' alone is an estimate of the error attained by a forest with an infinite number of trees. We recommend that users grow enough forests to make the 'excess.error' negligible.

Examples

# \donttest{
# Train a standard regression forest.
n <- 50
p <- 10
X <- matrix(rnorm(n * p), n, p)
Y <- X[, 1] * rnorm(n)
r.forest <- 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.forest, X.test)

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

# Predict with confidence intervals; growing more trees is now recommended.
r.forest <- regression_forest(X, Y, num.trees = 100)
r.pred <- predict(r.forest, X.test, estimate.variance = TRUE)
# }