MLPRegressor

Package

weka.classifiers.functions

Synopsis

Trains a multilayer perceptron with one hidden layer using WEKA's Optimization class by minimizing the squared error plus a quadratic penalty with the BFGS method. Note that all attributes are standardized, including the target. There are several parameters. The ridge parameter is used to determine the penalty on the size of the weights. The number of hidden units can also be specified. Note that large numbers produce long training times.Finally, it is possible to use conjugate gradient descent rather than BFGS updates, which may be faster for cases with many parameters. Nominal attributes are processed using the unsupervised NominalToBinary filter and missing values are replaced globally using ReplaceMissingValues.

This method is part of the multiLayerPerceptrons package for Weka 3.7.

Options

The table below describes the options available for MLPRegressor.

Option	Description
debug	If set to true, classifier may output additional info to the console.
numFunctions	The number of hidden units to use.
ridge	The ridge penalty factor for the quadratic penalty on the weights.
seed	The random number seed to be used.
useCGD	Whether to use conjugate gradient descent (potentially useful for many parameters).

Capabilities

The table below describes the capabilities of MLPRegressor.

Capability	Supported
Class	Numeric class, Missing class values
Attributes	Date attributes, Binary attributes, Nominal attributes, Empty nominal attributes, Unary attributes, Numeric attributes, Missing values
Min # of instances	1