This node applies an
model to an input dataset in order to predict anomalies or outliers.
output of the node will consist of the input and, depending on
settings, one or two appended
columns. One is the prediction which
contains normalized anomaly
score. The higher the score, the more
likely it is an anomaly. The
other (optionally) appended column
contains the mean length of
the predicted decision tree paths of each
observation. The shorter, the
more likely it is an anomaly.
Important note: All columns which have been used for training the model must be present in the incoming H2O frame as well.
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