Predict with a boosted regression forest.
Gets estimates of E[Y|X=x] using a trained regression forest.
## S3 method for class 'boosted_regression_forest' predict( object, newdata = NULL, boost.predict.steps = 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 |
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). |
A vector of predictions.
# 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)
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