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Valve is a simple to integrate sampling and code flow control library, which can perform sampling on an unknown size of identifiers. It was developed to fullfill the need to perform percentage based roll out of a certain feature for users.

This project provides a Java implementation of random forests. Random forests use training sets to build decision trees. Given an input (e.g. a person with age, gender, medical background, symptoms) the result (e.g. a disease) of which is unknown, random forests are able to predict the corresponding result.

Implements StackingC (more efficient version of stacking). For more information, see A.K. Seewald: How to Make Stacking Better and Faster While Also Taking Care of an Unknown Weakness. In: Nineteenth International Conference on Machine Learning, 554-561, 2002. Note: requires meta classifier to be a numeric prediction scheme