DecisionTable (3.7)

Options

DecisionTable Options

S: Full class name of search method, followed by its options. eg: "weka.attributeSelection.BestFirst -D 1" (default weka.attributeSelection.BestFirst)

X: Use cross validation to evaluate features. Use number of folds = 1 for leave one out CV. (Default = leave one out CV)

E: Performance evaluation measure to use for selecting attributes. (Default = accuracy for discrete class and rmse for numeric class)

I: Use nearest neighbour instead of global table majority.

R: Display decision table rules.

P: Specify a starting set of attributes. Eg. 1,3,5-7.

D: Direction of search. (default = 1).

N: Number of non-improving nodes to consider before terminating search.

S: Size of lookup cache for evaluated subsets. Expressed as a multiple of the number of attributes in the data set. (default = 1)

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, 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.

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.

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