posit::conf(2023) Workshop: Advanced tidymodels
Register now: http://pos.it/conf Instructor: Max Kuhn, Software Engineer, Posit Workshop Duration: 1-Day Workshop This workshop is for you if you: • have used tidymodels packages like recipes, rsample, and parsnip • are comfortable with tidyverse syntax (e.g. piping, mutates, pivoting) • have some experience with resampling and modeling (e.g., linear regression, random forests, etc.), but we don’t expect you to be an expert in these In this workshop, you will learn more about model optimization using the tune and finetune packages, including racing and iterative methods. You’ll be able to do more sophisticated feature engineering with recipes. Time permitting, model ensembles via stacking will be introduced. This course is focused on the analysis of tabular data and does not include deep learning methods. Participants who have completed the “Introduction to tidymodels” workshop will be well-prepared for this course. Participants who are new to tidymodels will benefit from taking the Introduction to tidymodels workshop before joining this one
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Transcript#
This transcript was generated automatically and may contain errors.
Hi, I'm Max Kuhn, I work at Posit. I'm here to entice you into joining us at the Advanced tidymodels Workshop at our conference. Now it says advanced in the title, you don't need to be an expert to join us. If you've taken the introductory tidymodels workshop, you'd be in a really good position to join us.
But if you haven't, if you've ever used tidymodels before, you'll probably be in good shape. So if you've fit a parsnip model or resampled it, or if you've used a recipe, things like that, you'll probably be in a good position to understand what we're going to go through.
Workshop topics
Now what are we going to talk about at the conference? So one thing we'll talk about is advanced recipe usage. So recipes are like pre-processors for your data before you put them in the model. And we'll talk about some sort of like pro tips for using recipes, as well as how to optimize them.
The second thing we'll talk about is model tuning. So you have models and some tuning parameters. We'll look at things like grid search, iterative search, some cool things called racing methods that you can use to optimize your model performance.
And then the third thing we'll talk about is calibration. So after you fit your model, you want to make sure that your classification probabilities or your fitted regression predictions are super consistent with the data that you built the model on. And so we have some new tools for that that we think are really cool.
And so we have some new tools for that that we think are really cool.
So anyway, we hope to see you in person in Chicago, and especially hope to see you at the EDS signals workshop. Thanks a lot.
