h2o.anomaly function

Anomaly Detection via H2O Deep Learning Model

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)

Arguments

  • 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

Returns an H2OFrame object containing the reconstruction MSE or the per-feature squared error.

Examples

## 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, seed = 1) 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)

See Also

h2o.deeplearning for making an H2OAutoEncoderModel.

  • Maintainer: Tomas Fryda
  • License: Apache License (== 2.0)
  • Last published: 2024-01-11