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H2O Isolation Forest Learner

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

Learns an Isolation Forest model using H2O . The model is learned unsupervised and can be used to detect anomalies or outliers by using the H2O Isolation Forest Predictor node.

Options

General Settings

Column selection
Select columns used for model training.
Ignore constant columns
Select to ignore constant columns.
Number of levels (tree depth)
Specify the maximum tree depth (max_depth) .
Number of models
Specify the number of trees (ntrees) .
Use static random seed
Select to use a static seed for randomization.

Algorithm Settings

Min (weighted) observations
Specify the minimum number of observations for a leaf (min_rows) .
Row sample rate (per tree)
Specify the row sampling rate (x-axis). The range is 0.0 to 1.0. Higher values may improve training accuracy. Test accuracy improves when either columns or rows are sampled. For details, refer to “Stochastic Gradient Boosting” (sample_rate) .
Row sample size (per tree)
Specify the number of randomly sampled rows used to train each tree. Higher values may improve training accuracy. Test accuracy improves when either columns or rows are sampled. For details, refer to “Stochastic Gradient Boosting” (sample_size) .
Column sample rate (per tree)
Specify the column sample rate per tree. This can be a value from 0.0 to 1.0 (col_sample_rate_per_tree) .
Relative change of column sample rate per level
This option specifies to change the column sampling rate as a function of the depth in the tree (col_sample_rate_change_per_level) .
M Tries
Specify the columns to randomly select at each level. If disabled, the number of variables is p/3 (where p is the number of included feature columns). The range is 1 to number of included feature columns. (mtries) .

Advanced Settings

Select categorical encoding
Specify one of the following encoding schemes for handling categorical features (categorical_encoding) .
Max runtime in seconds
Maximum allowed runtime in seconds for model training (max_runtime_secs) .

Input Ports

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H2O Frame with training data.

Output Ports

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H2O Isolation Forest model which can be used as input for the H2O Isolation Forest Predictor node.

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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