ClassificationViaRegression (3.7)

Class for doing classification using regression methods

Class is binarized and one regression model is built for each class value.For more information, see, for example

E.

Frank, Y.Wang, S.

Inglis, G.Holmes, I.H.

Witten (1998).Using model trees for classification.

Machine Learning.32(1):63-76.

(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

ClassificationViaRegression Options

D: If set, classifier is run in debug mode and may output additional info to the console

W: Full name of base classifier. (default: weka.classifiers.trees.M5P)

N: Use unpruned tree/rules

U: Use unsmoothed predictions

R: Build regression tree/rule rather than a model tree/rule

M: Set minimum number of instances per leaf (default 4)

L: Save instances at the nodes in the tree (for visualization purposes)

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: [Nominal attributes, Binary attributes, Unary attributes, Empty nominal attributes, Numeric attributes, Date attributes, String attributes, Relational attributes, Missing values, No class, Missing class values, Only multi-Instance data] 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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Further Links

Developers

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