KNIME WEKA nodes (3.7) version 4.2.0.v202007031307 by KNIME AG, Zurich, Switzerland
K-nearest neighbours classifier
Can select appropriate value of K based on cross-validation.Can also do distance weighting.
For more information, see
Aha, D.Kibler (1991).
Instance-based learning algorithms.Machine Learning.
(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.
I: Weight neighbours by the inverse of their distance (use when k > 1)
F: Weight neighbours by 1 - their distance (use when k > 1)
K: Number of nearest neighbours (k) used in classification. (Default = 1)
E: Minimise mean squared error rather than mean absolute error when using -X option with numeric prediction.
W: Maximum number of training instances maintained. Training instances are dropped FIFO. (Default = no window)
X: Select the number of nearest neighbours between 1 and the k value specified using hold-one-out evaluation on the training data (use when k > 1)
A: The nearest neighbour search algorithm to use (default: weka.core.neighboursearch.LinearNNSearch).
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:  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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