LMT (3.7)

Options

LMT Options

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

R: Split on residuals instead of class values

C: Use cross-validation for boosting at all nodes (i.e., disable heuristic)

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)

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.

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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