Towards a simplification of models using regression trees
نویسندگان
چکیده
منابع مشابه
Using regression trees to learn action models
Anyone who has ever driven a car on an icy road is aware of the impact the environment can have on our actions. In order to build effective plans, we must be aware of these environmental conditions and predict the effects they will have on our ability to act. In this paper, we present an application of regression trees that allows a robot to learn action models through experience so that it can...
متن کاملSimplification Methods for Model Trees with Regression and Splitting Nodes
Model trees are tree-based regression models that associate leaves with linear regression models. A new method for the stepwise induction of model trees (SMOTI) has been developed. Its main characteristic is the construction of trees with two types of nodes: regression nodes, which perform only straight-line regression, and splitting nodes, which partition the feature space. In this way, intern...
متن کاملEstimation of Count Data using Bivariate Negative Binomial Regression Models
Abstract Negative binomial regression model (NBR) is a popular approach for modeling overdispersed count data with covariates. Several parameterizations have been performed for NBR, and the two well-known models, negative binomial-1 regression model (NBR-1) and negative binomial-2 regression model (NBR-2), have been applied. Another parameterization of NBR is negative binomial-P regression mode...
متن کاملGeneration of regression trees using reinforcement learning
We present a novel methodology for regression trees generation that uses the reinforcement learning frame for learning efficient regression trees. We describe the basic variant of such a methodology that uses the Monte-Carlo method to explore the space of possible regression trees. Comparison with other methods of regression is performed and evaluated. Our algorithm is implemented as a software...
متن کاملPrediction of Ordinal Classes Using Regression Trees
This paper is devoted to the problem of learning to predict ordinal (i.e., ordered discrete) classes using classification and regression trees. We start with S-CART, a tree induction algorithm, and study various ways of transforming it into a learner for ordinal classification tasks. These algorithm variants are compared on a number of benchmark data sets to verify the relative strengths and we...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of The Royal Society Interface
سال: 2013
ISSN: 1742-5689,1742-5662
DOI: 10.1098/rsif.2012.0613