Analysis of missing data with random forests
نویسنده
چکیده
منابع مشابه
Random Survival Forests 1
We introduce random survival forests, a random forests method for the analysis of right-censored survival data. New survival splitting rules for growing survival trees are introduced, as is a new missing data algorithm for imputing missing data. A conservation-of-events principle for survival forests is introduced and used to define ensemble mortality, a simple interpretable measure of mortalit...
متن کاملRandom Survival Forests
We introduce random survival forests, a random forests method for the analysis of right-censored survival data. New survival splitting rules for growing survival trees are introduced, as is a new missing data algorithm for imputing missing data. A conservation-of-events principle for survival forests is introduced and used to define ensemble mortality, a simple interpretable measure of mortalit...
متن کاملImputation of Missing Values for Unsupervised Data Using the Proximity in Random Forests
This paper presents a new procedure that imputes missing values by random forests for unsupervised data. We found that it works pretty well compared with k-nearest neighbor (kNN) and rough imputations replacing the median of the variables. Moreover, this procedure can be expanded to semisupervised data sets. The rate of the correct classification is higher than that of other conventional method...
متن کاملVariable selection with Random Forests for missing data
Variable selection has been suggested for Random Forests to improve their efficiency of data prediction and interpretation. However, its basic element, i.e. variable importance measures, can not be computed straightforward when there is missing data. Therefore an extensive simulation study has been conducted to explore possible solutions, i.e. multiple imputation, complete case analysis and a n...
متن کاملRandom Forests with Missing Values in the Covariates
In Random Forests [2] several trees are constructed from bootstrapor subsamples of the original data. Random Forests have become very popular, e.g., in the fields of genetics and bioinformatics, because they can deal with high-dimensional problems including complex interaction effects. Conditional Inference Forests [8] provide an implementation of Random Forests with unbiased variable selection...
متن کامل