R: Predict with a multi regression forest

predict.multi_regression_forest {grf} | R Documentation

## Predict with a multi regression forest

### Description

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

### Usage

    ## S3 method for class 'multi_regression_forest'
    predict(object, newdata = NULL, num.threads = NULL, ...)

### 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 training. If set to NULL, the software automatically selects an appropriate amount.
`...` | Additional arguments (currently ignored).

### Value

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)

[Package grf version 2.1.0 Index]