| predict.multi_regression_forest {grf} | 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, ...)
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). |
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)