Level: Intermediate
Milo Page, JMP Research Statistician Developer, SAS

JMP Pro 14 includes a new Automated Data Imputation (ADI) utility, a versatile, empirically tuned, streaming, missing data imputation method. We recommend it for handling missing data as a pre-processing step to predictive model fitting. It empirically tunes to your data set to extract the underlying structure, even in the presence of missing data. It also respects training and validation partitions and interfaces seamlessly with predictive models. It is developed using powerful matrix completion methods with some added extensions for robustness and flexibility. This talk will focus on when ADI is appropriate and how to use it in JMP Pro 14. I will also outline a recommended workflow for processing data with missing values and demonstrate ADI’s performance on some examples.