Notebooks are used to document existing code, to quickly prototype and iterate on ideas, and as a medium of technical communication. Jupyter Notebooks play a critical role in in the workflow of many users. Together, let's think about the power that programming choices has to shape the mental model of the user, and the ways that we can responsibly document our modeling decisions to increase cross-language reproducibility. Can a helper function hide an important decision about tuning parameters? Can a slight change in argument input influence the way we describe a model? The answer is a resounding, "¡Sí!"ĭon't despair, though, because I will also provide advice for avoiding pitfalls when switching between languages or implementations. In this talk, I will share how the differences in structure and syntax between tidymodels and scikit-learn impacted student understanding. When I had to choose between them for a Machine Learning Course, I said: ¿Porque no los dos? (Why not both?)
The friendly competition between R and python has gifted us with two stellar packages for workflow-style predictive modeling: tidymodels in R, and scikit- learn in python.
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