RegressionByDiscretization
Package
weka.classifiers.meta
Synopsis
A regression scheme that employs any classifier on a copy of the data that has the class attribute discretized. The predicted value is the expected value of the mean class value for each discretized interval (based on the predicted probabilities for each interval). This class now also supports conditional density estimation by building a univariate density estimator from the target values in the training data, weighted by the class probabilities.
For more information on this process, see
Eibe Frank, Remco R. Bouckaert: Conditional Density Estimation with Class Probability Estimators. In: First Asian Conference on Machine Learning, Berlin, 65-81, 2009.
Options
The table below describes the options available for RegressionByDiscretization.
Option |
Description |
|---|---|
classifier |
The base classifier to be used. |
debug |
If set to true, classifier may output additional info to the console. |
deleteEmptyBins |
Whether to delete empty bins after discretization. |
estimatorType |
The density estimator to use. |
minimizeAbsoluteError |
Whether to minimize absolute error. |
numBins |
Number of bins for discretization. |
useEqualFrequency |
If set to true, equal-frequency binning will be used instead of equal-width binning. |
Capabilities
The table below describes the capabilities of RegressionByDiscretization.
Capability |
Supported |
|---|---|
Class |
Date class, Missing class values, Numeric class |
Attributes |
Numeric attributes, Nominal attributes, Empty nominal attributes, Date attributes, Binary attributes, Missing values, Unary attributes |
Min # of instances |
2 |