document: A general framework for accurate and fast regression by data summarization in random decision trees Predicting the values of continuous variable as a function of several independent variables is one of the most important problems for data mining . A very large number of regression methods , both parametric and nonparametric , have been proposed in the past . However , since the list is quite extensive and many of these models make rather explicit , strong yet different assumptions about the type of applicable problems and involve a lot of parameters and options , choosing the appropriate regression methodology and then specifying the parameter values is a none-trivial , sometimes frustrating , task for data mining practitioners . Choosing the inappropriate methodology can have rather disappointing results . This issue is against the general utility of data mining software . For example , linear regression methods are straightforward and well-understood . However , since the linear assumption is very strong , its performance is compromised for complicated non-linear problems . Kernel-based methods perform quite well if the kernel functions are selected correctly . In this paper , we propose a straightforward approach based on summarizing the training data using an ensemble of random decisions trees . It requires very little knowledge from the user , yet is applicable to every type of regression problem that we are currently aware of . We have experimented on a wide range of problems including those that parametric methods performwell , a large selection of benchmark datasets for nonparametric regression , as well as highly non-linear stochastic problems . Our results are either significantly better than or identical to many approaches that are known to perform well on these problems .
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