H2O MOJO Predictor (Isolation Forest)

This node applies an Isolation Forest MOJO to an input dataset in order to detect anomalies/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 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.

Input ports

1. Type: MOJO
   MOJO (AnomalyDetection)
   The MOJO. Its model category must be anomaly detection.
2. Type: Table
   Input Table
   Table for prediction. Missing values will be treated as NA .

Output ports

1. Type: Table
   Predicted data
   Table containing the predicted (normalized) anomaly score and, if selected, the mean length.