Anomaly Detection via H2O Deep Learning Model
Detect anomalies in an H2O dataset using an H2O deep learning model with auto-encoding.
h2o.anomaly(object, data, per_feature = FALSE)
object |
An H2OAutoEncoderModel object that represents the model to be used for anomaly detection. |
data |
An H2OFrame object. |
per_feature |
Whether to return the per-feature squared reconstruction error |
Returns an H2OFrame object containing the reconstruction MSE or the per-feature squared error.
h2o.deeplearning for making an H2OAutoEncoderModel.
## 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, training_frame = prostate, autoencoder = TRUE,
hidden = c(10, 10), epochs = 5)
prostate_anon = h2o.anomaly(prostate_dl, prostate)
head(prostate_anon)
prostate_anon_per_feature = h2o.anomaly(prostate_dl, prostate, per_feature = TRUE)
head(prostate_anon_per_feature)
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.