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 |