AODE

Package

weka.classifiers.bayes

Synopsis

AODE achieves highly accurate classification by averaging over all of a small space of alternative naive-Bayes-like models that have weaker (and hence less detrimental) independence assumptions than naive Bayes. The resulting algorithm is computationally efficient while delivering highly accurate classification on many learning tasks.

For more information, see

G. Webb, J. Boughton, Z. Wang (2005). Not So Naive Bayes: Aggregating One-Dependence Estimators. Machine Learning. 58(1):5-24.

Further papers are available at
http://www.csse.monash.edu.au/~webb/.

Can use an m-estimate for smoothing base probability estimates in place of the Laplace correction (via option -M).
Default frequency limit set to 1.

Available in Weka 3.6.x - 3.7.1. Available via the package management system for Weka >= 3.7.2 (averagedOneDependenceEstimators).

Options

The table below describes the options available for AODE.

Option	Description
debug	If set to true, classifier may output additional info to the console.
frequencyLimit	Attributes with a frequency in the train set below this value aren't used as parents.
useMEstimates	Use m-estimate instead of laplace correction.
weight	Set the weight for m-estimate.

Capabilities

The table below describes the capabilites of AODE.

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