KNIME WEKA nodes (3.7) version 4.3.1.v202101261634 by KNIME AG, Zurich, Switzerland
Implements Gaussian processes for regression without hyperparameter-tuning
To make choosing an appropriate noise level easier, this implementation applies normalization/standardization to the target attribute as well as the other attributes (if normalization/standardizaton is turned on).Missing values are replaced by the global mean/mode.
Nominal attributes are converted to binary ones.Note that kernel caching is turned off if the kernel used implements CachedKernel.
(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.
D: If set, classifier is run in debug mode and may output additional info to the console
L: Level of Gaussian Noise wrt transformed target. (default 1)
N: Whether to 0=normalize/1=standardize/2=neither. (default 0=normalize)
K: The Kernel to use. (default: weka.classifiers.functions.supportVector.PolyKernel)
D: Enables debugging output (if available) to be printed. (default: off)
no-checks: Turns off all checks - use with caution! (default: checks on)
C: The size of the cache (a prime number), 0 for full cache and -1 to turn it off. (default: 250007)
E: The Exponent to use. (default: 1.0)
L: Use lower-order terms. (default: no)
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, 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] min # Instance: 1
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:
A zipped version of the software site can be downloaded here.
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