KNIME WEKA nodes (3.7) version 4.0.0.v201905311239 by KNIME AG, Zurich, Switzerland
Class for generating a grafted (pruned or unpruned) C4.5 decision tree
For more information, see
Geoff Webb: Decision Tree Grafting From the All-Tests-But-One Partition.In: , San Francisco, CA, 1999.
(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.
U: Use unpruned tree.
C: Set confidence threshold for pruning. (default 0.25)
M: Set minimum number of instances per leaf. (default 2)
B: Use binary splits only.
S: Don't perform subtree raising.
L: Do not clean up after the tree has been built.
A: Laplace smoothing for predicted probabilities. (note: this option only affects initial tree; grafting process always uses laplace).
E: Relabel when grafting.
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, Missing class values] Dependencies:  min # Instance: 0
It shows the command line options according to the current classifier configuration and mainly serves to support the node's configuration via flow variables.
To use this node in KNIME, install KNIME Weka Data Mining Integration (3.7) from the following update site:
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