KNIME WEKA nodes version 2.10.2.v201911281246 by KNIME AG, Zurich, Switzerland
Dimensionality of training and test data is reduced by attribute selection before being passed on to a classifier.
(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, Date attributes, Missing values, Nominal class, Binary class, Numeric class, Date class, Missing class values] Dependencies: [Nominal attributes, Binary attributes, Unary attributes, Empty nominal attributes, Numeric attributes, Date attributes, String attributes, Relational attributes, Missing values, No class, Nominal class, Binary class, Unary class, Empty nominal class, Numeric class, Date class, String class, Relational class, Missing class values, Only multi-Instance data] min # Instance: 1
E: Full class name of attribute evaluator, followed by its options. eg: "weka.attributeSelection.CfsSubsetEval -L" (default weka.attributeSelection.CfsSubsetEval)
S: Full class name of search method, followed by its options. eg: "weka.attributeSelection.BestFirst -D 1" (default weka.attributeSelection.BestFirst)
D: If set, classifier is run in debug mode and may output additional info to the console
W: Full name of base classifier. (default: weka.classifiers.trees.J48)
U: Use unpruned tree.
C: Set confidence threshold for pruning. (default 0.25)
M: Set minimum number of instances per leaf. (default 2)
R: Use reduced error pruning.
N: Set number of folds for reduced error pruning. One fold is used as pruning set. (default 3)
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.
Q: Seed for random data shuffling (default 1).
To use this node in KNIME, install KNIME Weka Data Mining Integration (3.6) from the following update site:
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