IconWeightedClassifier (3.7)0 ×

KNIME WEKA nodes (3.7) version 3.6.0.v201805031010 by KNIME AG, Zurich, Switzerland

Classifier that weights a set of class association rules

That are association rules where exclusively one class attribute-value-pair is allowed in the consequence.No pruning is perfomed (except that the number of mined rules can be limited and from that set the user can choose to consider only the top ranked rules with the -L option).

For classification there are three options:a) all rules are equally weighted with 1 (default)

b) all rules are linearly weighted in a decreasing manner according to the sort order of the mining algorithmc) all rules are weighted with the inverse function 1/rank in sort order.

(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

WeightedClassifier Options

A: Full class name of class association rule miner to include, followed by scheme options. eg: "weka.associations.classification.CarApriori -N 100"

W: The type of the weighting function: inverse | linear | equal (default) eg: -T linear

L: The number of rules used for classification (default: all left after pruning)

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, 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, Nominal class, Binary class, Unary class, Empty nominal class, Numeric class, Date class, String class, Relational 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

Training data

Output Ports

Trained model

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.

Update Site

To use this node in KNIME, install KNIME WEKA nodes (3.7) from the following update site:

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