h2o.anomaly: Anomaly Detection via H2O Deep Learning Model

In h2o: R Interface for the 'H2O' Scalable Machine Learning Platform

Anomaly Detection via H2O Deep Learning Model

Description

Detect anomalies in an H2O dataset using an H2O deep learning model with auto-encoding.

Usage

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

Value

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

See Also

h2o.deeplearning for making an H2OAutoEncoderModel.

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)

h2o documentation built on May 29, 2024, 4:26 a.m.