KNIME WEKA nodes (3.7) version 4.2.0.v202007031307 by KNIME AG, Zurich, Switzerland
A Classifier that uses backpropagation to classify instances. This network can be built by hand, created by an algorithm or both
The network can also be monitored and modified during training time.The nodes in this network are all sigmoid (except for when the class is numeric in which case the the output nodes become unthresholded linear units).
(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.
L: Learning Rate for the backpropagation algorithm. (Value should be between 0 - 1, Default = 0.3).
M: Momentum Rate for the backpropagation algorithm. (Value should be between 0 - 1, Default = 0.2).
N: Number of epochs to train through. (Default = 500).
V: Percentage size of validation set to use to terminate training (if this is non zero it can pre-empt num of epochs. (Value should be between 0 - 100, Default = 0).
S: The value used to seed the random number generator (Value should be >= 0 and and a long, Default = 0).
E: The consequetive number of errors allowed for validation testing before the netwrok terminates. (Value should be > 0, Default = 20).
G: GUI will be opened. (Use this to bring up a GUI).
A: Autocreation of the network connections will NOT be done. (This will be ignored if -G is NOT set)
B: A NominalToBinary filter will NOT automatically be used. (Set this to not use a NominalToBinary filter).
H: The hidden layers to be created for the network. (Value should be a list of comma separated Natural numbers or the letters 'a' = (attribs + classes) / 2, 'i' = attribs, 'o' = classes, 't' = attribs .+ classes) for wildcard values, Default = a).
C: Normalizing a numeric class will NOT be done. (Set this to not normalize the class if it's numeric).
I: Normalizing the attributes will NOT be done. (Set this to not normalize the attributes).
R: Reseting the network will NOT be allowed. (Set this to not allow the network to reset).
D: Learning rate decay will occur. (Set this to cause the learning rate to decay).
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: 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.
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.
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