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h2o.SpeeDRF(x, y, data, classification = TRUE, validation, mtry = -1, ntree = 50,
depth = 50, sample.rate = 2/3, oobee = TRUE, importance = FALSE, nbins = 1024,
seed = -1, stat.type = "ENTROPY", classwt = NULL, sampling_strategy = "RANDOM",
strata_samples = NULL)
H2OParsedData
object containing the variables in the model.H2OParsedData
object indicating the validation dataset used to construct confusion matrix. If left blank, this defaults to the training data.sqrt(ncol(data))
, rounded down to the nearest integer.TRUE
, the algorithm will take longer to finish.)seed = -1
, one will automatically be generated by H2O.sampling_strategy = "STRATIFIED_LOCAL"
.H2OSpeeDRFModel
with slots key, data, valid (the validation dataset), and model, where the last is a list of the following components:beta = TRUE
in h2o.init
in order to use this method!This method runs random forest model building on a single node, as opposed to the multi-node implementation in h2o.randomForest.FV
.
H2OSpeeDRFModel
, h2o.randomForest
# Currently still in beta, so don't automatically run example
library(h2o)
localH2O = h2o.init(ip = "localhost", port = 54321, startH2O = TRUE, beta = TRUE)
irisPath = system.file("extdata", "iris.csv", package = "h2o")
iris.hex = h2o.importFile(localH2O, path = irisPath, key = "iris.hex")
h2o.SpeeDRF(x = c(2,3,4), y = 5, data = iris.hex, ntree = 50, depth = 100)
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