Addressing Measurement Error in Random Forests Using Quantitative Bias Analysis.

ABSTRACT: Although variables are often measured with error, the impact of measurement error on machine-learning predictions is seldom quantified. The purpose of this study was to assess the impact of measurement error on the performance of random-forest models and variable importance. First, we assessed the impact of misclassification (i.e., measurement error of categorical variables) of predictors on random-forest model performance (e.g., accuracy, sensitivity) and variable importance (mean decrease in accuracy) using data from the National Comorbidity Survey Replication (2001-2003). Second, we created simulated data sets in which we knew the true model performance and variable importance measures and could verify that quantitative bias analysis was recovering the truth in misclassified versions of

SUBMITTER: Jiang T 

PROVIDER: S-EPMC8408353 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature