NMSE errors (deviations from true values) on test sets for simulated datasets with simulated missingness: low correlation (0.1) and low AUROC (0.75).

<p>To facilitate visualisation, only four out of the twelve noise variables are included in the figure.</p>

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Bibliographic Details
Main Author:	Elena Albu (15181070) (author)
Other Authors:	Shan Gao (46743) (author), Laure Wynants (7546961) (author), Ben Van Calster (175284) (author)
Published:	2025
Subjects:	Genetics Cancer Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified missforestpredict &# 8212 eight simulated datasets linear regression imputation missforest imputation algorithm missing data imputation missing data missforestpredict algorithm simulated missingness mode imputation imputation models specific needs prediction time prediction settings outcome based new observations nearest neighbours model development input variables custom initialization convergence criterion categorical variables applied later allows users 48 scenarios
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