R: Predict with a boosted regression forest.

predict.boosted_regression_forest {grf} | R Documentation

## Predict with a boosted regression forest.

### Description

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

### Usage

    ## S3 method for class 'boosted_regression_forest'
    predict(
      object,
      newdata = NULL,
      boost.predict.steps = 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
`boost.predict.steps` | Number of boosting iterations to use for prediction. If blank, uses the full number of steps for the object given
`num.threads` | the number of threads used in prediction
`...` | Additional arguments (currently ignored).

### Value

A vector of predictions.

### Examples

    # Train a boosted regression forest.
    n <- 50
    p <- 10
    X <- matrix(rnorm(n * p), n, p)
    Y <- X[, 1] * rnorm(n)
    r.boosted.forest <- boosted_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.boosted.forest, X.test)

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

[Package grf version 1.2.0 Index]