predict.probability_forest function

Predict with a probability forest

Gets estimates of P[Y = k | X = x] using a trained forest.

## S3 method for class 'probability_forest'
predict(
  object,
  newdata = NULL,
  num.threads = NULL,
  estimate.variance = FALSE,
  ...
)

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 prediction. If set to NULL, the software automatically selects an appropriate amount.
• estimate.variance: Whether variance estimates for P[Y = k | X] are desired (for confidence intervals).
• ...: Additional arguments (currently ignored).

Returns

A list with attributes predictions: a matrix of predictions for each class, and optionally the attribute variance.estimates: a matrix of variance estimates for each class.

Examples

# Train a probability forest.
p <- 5
n <- 2000
X <- matrix(rnorm(n*p), n, p)
prob <- 1 / (1 + exp(-X[, 1] - X[, 2]))
Y <- as.factor(rbinom(n, 1, prob))
p.forest <- probability_forest(X, Y)

# Predict using the forest.
X.test <- matrix(0, 10, p)
X.test[, 1] <- seq(-1.5, 1.5, length.out = 10)
p.hat <- predict(p.forest, X.test, estimate.variance = TRUE)

# Plot the estimated success probabilities with 95 % confidence bands.
prob.test <- 1 / (1 + exp(-X.test[, 1] - X.test[, 2]))
p.true <- cbind(`0` = 1 - prob.test, `1` = prob.test)
plot(X.test[, 1], p.true[, "1"], col = 'red', ylim = c(0, 1))
points(X.test[, 1], p.hat$predictions[, "1"], pch = 16)
lines(X.test[, 1], (p.hat$predictions + 2 * sqrt(p.hat$variance.estimates))[, "1"])
lines(X.test[, 1], (p.hat$predictions - 2 * sqrt(p.hat$variance.estimates))[, "1"])

# Predict on out-of-bag training samples.
p.hat <- predict(p.forest)