paceRegression · Class for building pace regression linear models and using them for prediction. Under regularity conditions, pace regression is provably optimal when the number of coefficients tends to infinity. It consists of a group of estimators that are either overall optimal or optimal under certain conditions. The current work of the pace regression theory, and therefore also this implementation, do not handle: - missing values - non-binary nominal attributes - the case that n - k is small where n is the number of instances and k is the number of coefficients (the threshold used in this implmentation is 20) For more information see: Wang, Y (2000). A new approach to fitting linear models in high dimensional spaces. Hamilton, New Zealand. Wang, Y., Witten, I. H.: Modeling for optimal probability prediction. In: Proceedings of the Nineteenth International Conference in Machine Learning, Sydney, Australia, 650-657, 2002.

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• Latest Stable: 1.0.2.jar

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Version		Vulnerabilities	Size	Updated
1.0.x	paceRegression-1.0.2.jar		36.71 KB	Apr 26, 2012
	paceRegression-1.0.1.jar		36.70 KB	Apr 24, 2012

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weka.classifiers.functions

├─ weka.classifiers.functions.PaceRegression.class - [JAR]

weka.classifiers.functions.pace

├─ weka.classifiers.functions.pace.ChisqMixture.class - [JAR]

├─ weka.classifiers.functions.pace.DiscreteFunction.class - [JAR]

├─ weka.classifiers.functions.pace.MixtureDistribution.class - [JAR]

├─ weka.classifiers.functions.pace.NormalMixture.class - [JAR]

├─ weka.classifiers.functions.pace.PaceMatrix.class - [JAR]