View source: R/multi_regression_forest.R
predict.multi_regression_forest | R Documentation |
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, ...)
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. |
drop |
If TRUE, coerce the prediction result to the lowest possible dimension. Default is FALSE. |
... |
Additional arguments (currently ignored). |
A list containing 'predictions': a matrix of predictions for each outcome.
# 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)
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