Total Tidy Tuning Techniques

Many models have structural parameters that cannot be directly estimated from the data. These tuning parameters can have a significant effect on model performance and require some mechanism for...

Total Tidy Tuning Techniques

February 12, 2020

Many models have structural parameters that cannot be directly estimated from the data. These tuning parameters can have a significant effect on model performance and require some mechanism for finding reasonable values. The tune and workflow packages enable tidymodels users to optimize these parameters using a variety of efficient grid search methods as well as with iterative search techniques (such as Bayesian optimization).


About the speaker

Max Kuhn

Max Kuhn is a software engineer at RStudio. He is currently working on improving R's modeling capabilities. He was a 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 and is an Associate Editor for the Journal of Statistical Software. 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.