KNIME WEKA nodes version 2.10.2.v202003301209 by KNIME AG, Zurich, Switzerland
Class for bagging a classifier to reduce variance. Can do classification and regression depending on the base learner. For more information, see Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
(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
P: Size of each bag, as a percentage of the training set size. (default 100)
O: Calculate the out of bag error.
S: Random number seed. (default 1)
I: Number of iterations. (default 10)
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.REPTree)
M: Set minimum number of instances per leaf (default 2).
V: Set minimum numeric class variance proportion of train variance for split (default 1e-3).
N: Number of folds for reduced error pruning (default 3).
S: Seed for random data shuffling (default 1).
P: No pruning.
L: Maximum tree depth (default -1, no maximum)
To use this node in KNIME, install KNIME Weka Data Mining Integration (3.6) from the following update site:
A zipped version of the software site can be downloaded here.
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