SimpleCart (3.6)

Class implementing minimal cost-complexity pruning. Note when dealing with missing values, use "fractional instances" method instead of surrogate split method. For more information, see: Leo Breiman, Jerome H. Friedman, Richard A. Olshen, Charles J. Stone (1984). Classification and Regression Trees. Wadsworth International Group, Belmont, California.

(based on WEKA 3.6)

For further options, click the 'More' - button in the dialog.

All weka dialogs have a panel where you can specify classifier-specific parameters.


Class 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, Missing values, Nominal class, Binary class] Dependencies: [] min # Instance: 1

Classifier Options

S: Random number seed. (default 1)

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

M: The minimal number of instances at the terminal nodes. (default 2)

N: The number of folds used in the minimal cost-complexity pruning. (default 5)

U: Don't use the minimal cost-complexity pruning. (default yes).

H: Don't use the heuristic method for binary split. (default true).

A: Use 1 SE rule to make pruning decision. (default no).

C: Percentage of training data size (0-1]. (default 1).

Input Ports

Training data

Output Ports

Trained classifier

Popular Predecessors

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Popular Successors

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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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