VSURF: An R Package for Variable Selection Using Random Forests
نویسندگان
چکیده
منابع مشابه
VSURF: An R Package for Variable Selection Using Random Forests
This paper describes the R package VSURF. Based on random forests, and for both regression and classification problems, it returns two subsets of variables. The first is a subset of important variables including some redundancy which can be relevant for interpretation, and the second one is a smaller subset corresponding to a model trying to avoid redundancy focusing more closely on the predict...
متن کاملVariable selection using random forests
This paper proposes, focusing on random forests, the increasingly used statistical method for classification and regression problems introduced by Leo Breiman in 2001, to investigate two classical issues of variable selection. The first one is to find important variables for interpretation and the second one is more restrictive and try to design a good prediction model. The main contribution is...
متن کاملVariable Selection Using Random Forests
One of the main topic in the development of predictive models is the identification of variables which are predictors of a given outcome. Automated model selection methods, such as backward or forward stepwise regression, are classical solutions to this problem, but are generally based on strong assumptions about the functional form of the model or the distribution of residuals. In this paper a...
متن کاملFWDselect: An R Package for Variable Selection in Regression Models
In multiple regression models, when there are a large number (p) of explanatory variables which may or may not be relevant for predicting the response, it is useful to be able to reduce the model. To this end, it is necessary to determine the best subset of q (q ≤ p) predictors which will establish the model with the best prediction capacity. FWDselect package introduces a new forward stepwiseb...
متن کاملrknn: an R Package for Parallel Random KNN Classification with Variable Selection
Random KNN (RKNN) is a novel generalization of traditional nearest-neighbor modeling. Random KNN consists of an ensemble of base k-nearest neighbor models, each constructed from a random subset of the input variables. A collection of r such base classifiers is combined to build the final Random KNN classifier. Since the base classifiers can be computed independently of one another, the overall ...
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: The R Journal
سال: 2015
ISSN: 2073-4859
DOI: 10.32614/rj-2015-018