predict.multi_regression_forest function

Predict with a multi regression forest

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

## S3 method for class 'multi_regression_forest'
predict(object, newdata = NULL, num.threads = NULL, drop = 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.
• num.threads: Number of threads used in prediction. If set to NULL, the software automatically selects an appropriate amount.
• drop: If TRUE, coerce the prediction result to the lowest possible dimension. Default is FALSE.
• ...: Additional arguments (currently ignored).

Returns

A list containing predictions: a matrix of predictions for each outcome.

Examples

# Train a standard regression forest.
n <- 500
p <- 5
X <- matrix(rnorm(n * p), n, p)
Y <-  X[, 1, drop = FALSE] %*% cbind(1, 2) + rnorm(n)
mr.forest <- multi_regression_forest(X, Y)

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

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