Random forest missing data algorithms
By Fei Tang, Hemant Ishwaran. Statistical analysis and data mining, 2017
679 citations recorded by the source corpus.
Random forest (RF) missing data algorithms are an attractive approach for imputing missing data. They have the desirable properties of being able to handle mixed types of missing data, they are adaptive to interactions and nonlinearity, and they have the potential to scale to big data settings. Currently there are many different RF imputation algorithms, but relatively little guidance about their efficacy. Using a large, diverse collection of data sets, imputation performance of various RF algorithms was assessed under different missing data mechanisms. Algorithms included proximity imputation, on the fly imputation, and imputation utilizing multivariate unsupervised and supervised splitting—the latter class representing a generalization of a new promising imputation algorithm called missForest. Our findings reveal RF imputation to be generally robust with performance improving with increasing correlation. Performance was good under moderate to high missingness, and even (in certain cases) when data was missing not at random.
Subfields: Artificial Intelligence, Information Systems, Signal Processing
View Paper: https://www.semanticscholar.org/paper/db9924032f6d61bc649097f35b6729dbcbf1976b
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In Artificial Intelligence
- Hemant Ishwaran
- Lancelot F. James
- Michael I. Jordan
- David B. Dunson
- Yee Whye Teh
- Lawrence Carin
- Mahmoud Zarepour
- Timothy E. Hanson
In Information Systems
In Signal Processing
Citations in the corpus
- A survey on missing data in machine learning (2021, background): “Forests can achieve competitive or even superior prediction strengths in comparison to well established approaches such as regression and support vector machines [93].”
- Estimating Individual Treatment Effect in Observational Data Using Random Forest Methods (2017, background): “Even for a medical discipline steeped in a tradition of randomized trials, the evidence basis for only a few guidelines is based on randomized trials (Tricoci et al. 2009).”