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Locally weighted learning. Uses an instance-based algorithm to assign instance weights which are then used by a specified WeightedInstancesHandler.
Can do classification (e.g. using naive Bayes) or regression (e.g. using linear regression).

For more info, see

Eibe Frank, Mark Hall, Bernhard Pfahringer: Locally Weighted Naive Bayes. In: 19th Conference in Uncertainty in Artificial Intelligence, 249-256, 2003.

C. Atkeson, A. Moore, S. Schaal (1996). Locally weighted learning. AI Review..


The table below describes the options available for LWL.

Option Description
KNN How many neighbours are used to determine the width of the weighting function (<= 0 means all neighbours).
classifier The base classifier to be used.
debug If set to true, classifier may output additional info to the console.
nearestNeighbourSearchAlgorithm The nearest neighbour search algorithm to use (Default: LinearNN).
weightingKernel Determines weighting function. [0 = Linear, 1 = Epnechnikov,2 = Tricube, 3 = Inverse, 4 = Gaussian and 5 = Constant. (default 0 = Linear)].


The table below describes the capabilites of LWL.

Capability Supported
Class Numeric class, Missing class values, Binary class, Date class, Nominal class
Attributes Numeric attributes, Binary attributes, Missing values, Empty nominal attributes, Nominal attributes, Date attributes, Unary attributes
Min # of instances 0

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