On the Consistency of a Random Forest Algorithm in the Presence of Missing Entries
Statistics Theory
2023-09-01 v2 Statistics Theory
Abstract
This paper tackles the problem of constructing a non-parametric predictor when the latent variables are given with incomplete information. The convenient predictor for this task is the random forest algorithm in conjunction to the so-called CART criterion. The proposed technique enables a partial imputation of the missing values in the data set in a way that suits both a consistent estimator of the regression function as well as a partial recovery of the missing values. A proof of the consistency of the random forest estimator is given in the case where each latent variable is missing completely at random (MCAR).
Cite
@article{arxiv.2011.05433,
title = {On the Consistency of a Random Forest Algorithm in the Presence of Missing Entries},
author = {Irving Gómez-Méndez and Emilien Joly},
journal= {arXiv preprint arXiv:2011.05433},
year = {2023}
}