M5Rules (3.7)

Generates a decision list for regression problems using separate-and-conquer

In each iteration it builds a model tree using M5 and makes the "best" leaf into a rule.

For more information see:

Geoffrey Holmes, Mark Hall, Eibe Frank: Generating Rule Sets from Model Trees.In: Twelfth Australian Joint Conference on Artificial Intelligence, 1-12, 1999.

Ross J.

Quinlan: Learning with Continuous Classes.In: 5th Australian Joint Conference on Artificial Intelligence, Singapore, 343-348, 1992.

Y.

Wang, I.H.

Witten: Induction of model trees for predicting continuous classes.In: Poster papers of the 9th European Conference on Machine Learning, 1997.

(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

M5Rules Options

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)

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, Numeric class, Date 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

Icon
Training data

Output Ports

Icon
Trained model

Popular Predecessors

  • No recommendations found

Popular Successors

  • No recommendations found

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

  • No workflows found

Links

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 Weka Data Mining Integration (3.7) from the below update site following our NodePit Product and Node Installation Guide:

v5.2

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

Plugin provider: KNIME AG, Zurich, Switzerland
Plugin version: 5.2.0.v202310301556
On NodePit since: 2023-12-06
Last update: 2024-04-22
KNIME versions: Since v3.6

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

Try NodePit Runner!