Modeling and machine learning in R involve a bewildering array of heterogeneous packages, and establishing good statistical practice is challenging in any language. The tidymodels (tidymodels.org) collection of packages offers a consistent, flexible framework for your modeling and machine learning work to address these problems. In this talk, we’ll focus on three specific reasons to consider using tidymodels. We will start with model characteristics themselves, move to the wise management of your data budget, and finish with feature engineering.
Julia Silge is a data scientist and software engineer at RStudio PBC where she works on open source modeling tools. She is an author, an international keynote speaker, and a real-world practitioner focusing on data analysis and machine learning practice. Julia loves text analysis, making beautiful charts, and communicating about technical topics with diverse audiences.
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R-Ladies is a worldwide organization whose mission is to promote gender diversity in the R community.
A question we often get is "Are men welcome to attend this event?"
As a diversity initiative, the mission of R-Ladies is to achieve proportionate representation by encouraging, inspiring, and empowering people of genders currently underrepresented in the R community. R-Ladies’ primary focus, therefore, is on supporting minority gender R enthusiasts to achieve their programming potential, by building a collaborative global network of R leaders, mentors, learners, and developers to facilitate individual and collective progress worldwide.
**As such, we strongly encourage cis-men who attend this event come as guests of R-Ladies who are minority genders (including but not limited to cis/trans women, trans men, non-binary, genderqueer, agender).**
All attendees are strongly encouraged to review the R-Ladies Code of Confuct (https://rladies.org/code-of-conduct) and must abide by it.
