Predict with a quantile forest
Gets estimates of the conditional quantiles of Y given X using a trained forest.
## S3 method for class 'quantile_forest' predict(object, newdata = NULL, quantiles = 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. |
quantiles |
Vector of quantiles at which estimates are required. If NULL, the quantiles used to train the forest is used. Default is NULL. |
num.threads |
Number of threads used in training. If set to NULL, the software automatically selects an appropriate amount. |
... |
Additional arguments (currently ignored). |
Predictions at each test point for each desired quantile.
# Train a quantile forest. n <- 50 p <- 10 X <- matrix(rnorm(n * p), n, p) Y <- X[, 1] * rnorm(n) q.forest <- quantile_forest(X, Y, quantiles = c(0.1, 0.5, 0.9)) # Predict on out-of-bag training samples. q.pred <- predict(q.forest) # Predict using the forest. X.test <- matrix(0, 101, p) X.test[, 1] <- seq(-2, 2, length.out = 101) q.pred <- predict(q.forest, X.test)
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