Feature Generation via H2O Deep Learning
Extract the non-linear feature from an H2O data set using an H2O deep learning model.
h2o.deepfeatures(object, data, layer)
object
: An H2OModel object that represents the deep learning model to be used for feature extraction.data
: An H2OFrame object.layer
: Index (integer) of the hidden layer to extractReturns an H2OFrame object with as many features as the number of units in the hidden layer of the specified index.
## Not run: library(h2o) h2o.init() prostate_path = system.file("extdata", "prostate.csv", package = "h2o") prostate = h2o.importFile(path = prostate_path) prostate_dl = h2o.deeplearning(x = 3:9, y = 2, training_frame = prostate, hidden = c(100, 200), epochs = 5) prostate_deepfeatures_layer1 = h2o.deepfeatures(prostate_dl, prostate, layer = 1) prostate_deepfeatures_layer2 = h2o.deepfeatures(prostate_dl, prostate, layer = 2) head(prostate_deepfeatures_layer1) head(prostate_deepfeatures_layer2) ## End(Not run)
h2o.deeplearning
for making H2O Deep Learning models.