## Package

**weka.classifiers.lazy**

## Synopsis

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..

## Options

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)]. |

## Capabilities

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 |