Package

weka.classifiers.functions

Synopsis

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.

Options

The table below describes the options available for PaceRegression.

Option

Description

debug

Output debug information to the console.

estimator

The estimator to use.

eb – Empirical Bayes estimator for noraml mixture (default)
nested – Optimal nested model selector for normal mixture
subset – Optimal subset selector for normal mixture
pace2 – PACE2 for Chi-square mixture
pace4 – PACE4 for Chi-square mixture
pace6 – PACE6 for Chi-square mixture
ols – Ordinary least squares estimator
aic – AIC estimator
bic – BIC estimator
ric – RIC estimator
olsc – Ordinary least squares subset selector with a threshold

threshold

Threshold for the olsc estimator.

Capabilities

The table below describes the capabilites of PaceRegression.

Capability	Supported
Class	Numeric class, Date class, Missing class values
Attributes	Empty nominal attributes, Numeric attributes, Unary attributes, Binary attributes
Min # of instances	1