KNIME WEKA nodes (3.7) version 4.4.0.v202104271009 by KNIME AG, Zurich, Switzerland
AODE achieves highly accurate classification by averaging over all of a small space of alternative naive-Bayes-like models that have weaker (and hence less detrimental) independence assumptions than naive Bayes
The resulting algorithm is computationally efficient while delivering highly accurate classification on many learning tasks.
For more information, see
Boughton, Z.Wang (2005).
Not So Naive Bayes: Aggregating One-Dependence Estimators.Machine Learning.
Further papers are available athttp://www.csse.monash.edu.au/~webb/.
Use m-estimate for smoothing base probability estimates witha default of 1 (m value can changed via option -M).
Default mode is non-incremental that is probabilites are computed at learning time.An incremental version can be used via option -I.
Default frequency limit set to 1.
Subsumption Resolution can be achieved by using -S option.Weighting of SPODE can be done by using -W option.
Weights are calculated based on mutual information between attribute and the class.The weighting scheme is developed by L.
Jiang and H.Zhang
(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: Output debugging information
F: Impose a frequency limit for superParents (default is 1)
M: Specify a weight to use with m-estimate (default is 1)
S: Specify a critical value for specialization-generalilzation SR (default is 100)
W: Specify if to use weighted AODE
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, Nominal class, Binary 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:
A zipped version of the software site can be downloaded here.
You don't know what to do with this link? Read our NodePit Product and Node Installation Guide that explains you in detail how to install nodes to your KNIME Analytics Platform.
You want to see the source code for this node? Click the following button and we’ll use our super-powers to find it for you.
Do you have feedback, questions, comments about NodePit, want to support this platform, or want your own nodes or workflows listed here as well? Do you think, the search results could be improved or something is missing? Then please get in touch! Alternatively, you can send us an email to email@example.com, follow @NodePit on Twitter, or chat on Gitter!
Please note that this is only about NodePit. We do not provide general support for KNIME — please use the KNIME forums instead.