H2O Isolation Forest Learner

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

: H2O Frame with training data.

Output Ports

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

Popular Predecessors

Popular Successors

Views

This node has no views

Workflows

Developers

You want to see the source code for this node? Click the following button and we’ll use our super-powers to find it for you.

Installation

To use this node in KNIME, install the extension KNIME H2O Machine Learning Integration from the below update site following our NodePit Product and Node Installation Guide:

v5.4

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

Plugin provider: KNIME AG, Zurich, Switzerland

Plugin version: 5.4.0.v202407231410

On NodePit since: 2024-12-06

Last update: 2024-12-17

KNIME versions: Since v4.0

Deploy, schedule, execute, and monitor your KNIME workflows locally, in the cloud or on-premises – with our brand new NodePit Runner.

Try NodePit Runner!