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, String attributes, Relational attributes, Missing values, Nominal class, Binary class]
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: 0
F: Full class name of filter to use, followed
by filter options.
eg: "weka.filters.unsupervised.attribute.Remove -V -R 1,2"
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).
