Predict with a custom forest.
Predict with a custom forest.
## S3 method for class 'custom_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). |
Vector of predictions.
# Train a custom forest. n <- 50 p <- 10 X <- matrix(rnorm(n * p), n, p) Y <- X[, 1] * rnorm(n) c.forest <- custom_forest(X, Y) # Predict using the forest. X.test <- matrix(0, 101, p) X.test[, 1] <- seq(-2, 2, length.out = 101) c.pred <- predict(c.forest, X.test)
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