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arXiv · 2011.05433

On the Consistency of a Random Forest Algorithm in the Presence of Missing Entries

Irving Gómez-Méndez·Emilien Joly
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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).

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Irving Gómez-Méndez, Emilien Joly. 2020-11-10. On the Consistency of a Random Forest Algorithm in the Presence of Missing Entries. https://doi.org/10.1080/10485252.2023.2219783

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