This node applies an Isolation Forest model to an input dataset in order to predict anomalies or outliers. The output of the node will consist of the input and, depending on the settings, one or two appended columns. One is the prediction which contains normalized anomaly scores. The higher the score, the more likely it is an anomaly. The other (optionally) appended column contains the mean lengths 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.