H2O Isolation Forest Predictor


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.

Input Ports

  1. Type: H2O Model
    H2O Isolation Forest model, e.g. an Isolation Forest model.
  2. Type: H2O Frame
    H2O frame with data that is predicted

Output Ports

  1. Type: H2O Frame
    H2O Frame with the predictions.


This node is part of the extension

KNIME H2O Machine Learning Integration


Short Link

Drag node into KNIME Analytics Platform