The following node is available in the Open Source KNIME predictive analytics and data mining platform version 2.7.1. Discover over 1000 other nodes, as well as enterprise functionality at http://knime.com.
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 3.6)
For further options, click the 'More' - button in the dialog.
All weka dialogs have a panel where you can specify classifier-specific parameters.
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
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.
0 | Training data |
0 | Trained classifier |