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. |
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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) # }