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H2O Isolation Forest Predictor

KNIME H2O Machine Learning Integration version 4.3.0.v202012011122 by KNIME AG, Zurich, Switzerland

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 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.

Options

Settings

Prediction column name
Change the name of the prediction column.
Append column containing mean length
Select to append an extra column that contains the mean length of the predicted decision tree paths for each observation.
Mean length column name
Change the name of the created column that contains the mean length.

Input Ports

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H2O Isolation Forest model, e.g. an Isolation Forest model.
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H2O frame with data that is predicted

Output Ports

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H2O Frame with the predictions.

Best Friends (Incoming)

Best Friends (Outgoing)

Workflows

Installation

To use this node in KNIME, install KNIME H2O Machine Learning Integration from the following update site:

KNIME 4.3

A zipped version of the software site can be downloaded here.

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Developers

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