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.
(based on WEKA 3.7)
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