**Creating model en mass with workflowsets**
The tidymodels packages allow users to create and optimize models and their pre-processing steps (e.g. PCA, feature encodings, etc). With new data, it is common to try a variety of models to determine what works best. The workflowsets package enables users to easily evaluate, rank, and even combine (with the stacks package) myriad combinations of models and pre-processors.
Bio:
Max Kuhn is a software engineer at RStudio. He is currently working on improving R's modeling capabilities. He was a Senior Director of Nonclinical Statistics at Pfizer Global R&D in Connecticut. He was applying models in the pharmaceutical and diagnostic industries for over 18 years. Max has a Ph.D. in Biostatistics.
Max is the author of numerous R packages for techniques in machine learning and reproducible research. He, and Kjell Johnson, wrote the book Applied Predictive Modeling, which won the Ziegel award from the American Statistical Association, which recognizes the best book reviewed in Technometrics in 2015. Their latest book, Feature Engineering and Selection, was published in 2019.
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