Machine learning with tabular data using the tidymodels framework.
Description
This workshop will teach you core tidymodels packages and their uses: data splitting/resampling with rsample, model fitting with parsnip, measuring model performance with yardstick, and basic pre-processing with recipes. Time permitting, you’ll be introduced to model optimization using the tune package. You’ll learn tidymodels syntax as well as the process of predictive modeling for tabular data.
Audience
This workshop is for you if you:
- are comfortable using tidyverse packages to read data into R, transform and reshape data, and make a variety of graphs, and
- have had some exposure to basic statistical concepts such as linear models, residuals, etc.
Intermediate or expert familiarity with modeling or machine learning is not required. Interested students who have intermediate or expert familiarity with modeling or machine learning may be interested in the Advanced tidymodels workshop.
Instructor(s)
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Hannah Frick is a software engineer and statistician on the tidymodels team at Posit. She holds a PhD in statistics and has worked in data science consultancy as well as interdisciplinary research at University College London in cooperation with Team GB Hockey. | |
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Simon Couch works on software for statistical modeling on the tidymodels team at Posit. With a background in statistics and sociology, Simon is passionate about free and open source software and data pedagogy. He is an author and maintainer of several R packages, including the stacks package, which was awarded the 2021 John M. Chambers Statistical Software Award. |