FT (3.7)

Classifier for building 'Functional trees', which are classification trees that could have logistic regression functions at the inner nodes and/or leaves

The algorithm can deal with binary and multi-class target variables, numeric and nominal attributes and missing values.

For more information see:

Joao Gama (2004). Functional Trees.

Niels Landwehr, Mark Hall, Eibe Frank (2005). Logistic Model Trees.

(based on WEKA 3.7)

For further options, click the 'More' - button in the dialog.

All weka dialogs have a panel where you can specify classifier-specific parameters.

Options

FT Options

B: Binary splits (convert nominal attributes to binary ones)

P: Use error on probabilities instead of misclassification error for stopping criterion of LogitBoost.

I: Set fixed number of iterations for LogitBoost (instead of using cross-validation)

F: Set Funtional Tree type to be generate: 0 for FT, 1 for FTLeaves and 2 for FTInner

M: Set minimum number of instances at which a node can be split (default 15)

W: Set beta for weight trimming for LogitBoost. Set to 0 (default) for no weight trimming.

A: The AIC is used to choose the best iteration.

Select target column
Choose the column that contains the target variable.
Preliminary Attribute Check

The Preliminary Attribute Check tests the underlying classifier against the DataTable specification at the inport of the node. Columns that are compatible with the classifier are marked with a green 'ok'. Columns which are potentially not compatible are assigned a red error message.

Important: If a column is marked as 'incompatible', it does not necessarily mean that the classifier cannot be executed! Sometimes, the error message 'Cannot handle String class' simply means that no nominal values are available (yet). This may change during execution of the predecessor nodes.

Capabilities: [Nominal attributes, Binary attributes, Unary attributes, Empty nominal attributes, Numeric attributes, Date attributes, Missing values, Nominal class, Binary class, Missing class values] Dependencies: [] min # Instance: 1

Command line options

It shows the command line options according to the current classifier configuration and mainly serves to support the node's configuration via flow variables.

Additional Options

Select optional vector column
If the input table contains vector columns (e.g. double vector), the one to use can be selected here. This vector column will be used as attributes only and all other columns, except the target column, will be ignored.
Keep training instances
If checked, all training instances will be kept and stored with the classifier model. It is useful to calculate additional evaluation measures (see Weka Predictor) that make use of class prior probabilities. If no evaluation is performed or those measures are not required, it is advisable to NOT keep the training instances.

Input Ports

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Training data

Output Ports

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Trained model

Popular Predecessors

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Popular Successors

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Views

Weka Node View
Each Weka node provides a summary view that provides information about the classification. If the test data contains a class column, an evaluation is generated.

Workflows

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Links

Developers

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Installation

To use this node in KNIME, install the extension KNIME Weka Data Mining Integration (3.7) from the below update site following our NodePit Product and Node Installation Guide:

v5.3

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

Plugin provider: KNIME AG, Zurich, Switzerland
Plugin version: 5.3.0.v202312081535
On NodePit since: 2024-07-09
Last update: 2024-11-14
KNIME versions: Since v3.6

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