SL.ranger: SL wrapper for ranger

In SuperLearner: Super Learner Prediction

SL wrapper for ranger

Description

Ranger is a fast implementation of Random Forest (Breiman 2001) or recursive partitioning, particularly suited for high dimensional data.

Extending code by Eric Polley from the SuperLearnerExtra package.

Usage

SL.ranger(Y, X, newX, family, obsWeights, num.trees = 500,
  mtry = floor(sqrt(ncol(X))), write.forest = TRUE,
  probability = family$family == "binomial",
  min.node.size = ifelse(family$family == "gaussian", 5, 1), replace = TRUE,
  sample.fraction = ifelse(replace, 1, 0.632), num.threads = 1,
  verbose = T, ...)

Arguments

Y	Outcome variable
X	Training dataframe
newX	Test dataframe
family	Gaussian or binomial
obsWeights	Observation-level weights
num.trees	Number of trees.
mtry	Number of variables to possibly split at in each node. Default is the (rounded down) square root of the number variables.
write.forest	Save ranger.forest object, required for prediction. Set to FALSE to reduce memory usage if no prediction intended.
probability	Grow a probability forest as in Malley et al. (2012).
min.node.size	Minimal node size. Default 1 for classification, 5 for regression, 3 for survival, and 10 for probability.
replace	Sample with replacement.
sample.fraction	Fraction of observations to sample. Default is 1 for sampling with replacement and 0.632 for sampling without replacement.
num.threads	Number of threads to use.
verbose	If TRUE, display additional output during execution.
...	Any additional arguments, not currently used.

References

Breiman, L. (2001). Random forests. Machine learning 45:5-32.

Wright, M. N. & Ziegler, A. (2016). ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R. Journal of Statistical Software, in press. http://arxiv.org/abs/1508.04409.

See Also

SL.ranger ranger predict.ranger

Examples

data(Boston, package = "MASS")
Y = Boston$medv
# Remove outcome from covariate dataframe.
X = Boston[, -14]

set.seed(1)

# Use only 2 CV folds to speed up example.
sl = SuperLearner(Y, X, family = gaussian(), cvControl = list(V = 2),
                 SL.library = c("SL.mean", "SL.ranger"))
sl

pred = predict(sl, X)
summary(pred$pred)

SuperLearner documentation built on May 29, 2024, 5:25 a.m.