predict.regression_forest function

Predict with a regression forest

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

## S3 method for class '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 prediction. 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).

Returns

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

# 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)