MultilayerPerceptron (3.7)

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.


MultilayerPerceptron Options

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).

Select target column
Choose the column that contains the target variable.
Preliminary Attribute Check

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

Command line options

It shows the command line options according to the current classifier configuration and mainly serves to support the node's configuration via flow variables.

Additional Options

Select optional vector column
If the input table contains vector columns (e.g. double vector), the one to use can be selected here. This vector column will be used as attributes only and all other columns, except the target column, will be ignored.
Keep training instances
If checked, all training instances will be kept and stored with the classifier model. It is useful to calculate additional evaluation measures (see Weka Predictor) that make use of class prior probabilities. If no evaluation is performed or those measures are not required, it is advisable to NOT keep the training instances.

Input Ports

Training data

Output Ports

Trained model

Popular Predecessors

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Popular Successors

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Weka Node View
Each Weka node provides a summary view that provides information about the classification. If the test data contains a class column, an evaluation is generated.


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