Split an existing H2O data set according to user-specified ratios. The number of subsets is always 1 more than the number of given ratios. Note that this does not give an exact split. H2O is designed to be efficient on big data using a probabilistic splitting method rather than an exact split. For example, when specifying a split of 0.75/0.25, H2O will produce a test/train split with an expected value of 0.75/0.25 rather than exactly 0.75/0.25. On small datasets, the sizes of the resulting splits will deviate from the expected value more than on big data, where they will be very close to exact.
h2o.splitFrame(data, ratios = 0.75, destination_frames, seed = -1)
| data | An H2OFrame object representing the dataste to split. |
|---|---|
| ratios | A numeric value or array indicating the ratio of total rows contained in each split. Must total up to less than 1. |
| destination_frames | An array of frame IDs equal to the number of ratios specified plus one. |
| seed | Random seed. |
Returns a list of split H2OFrame's
# NOT RUN { library(h2o) h2o.init() irisPath <- system.file("extdata", "iris.csv", package = "h2o") iris.hex <- h2o.importFile(path = irisPath) iris.split <- h2o.splitFrame(iris.hex, ratios = c(0.2, 0.5)) head(iris.split[[1]]) summary(iris.split[[1]]) # }