Max Kuhn
where else to find them
no profile links on file
what they talk about
where they keep going back
- rstudio::conf( )2 editions2019–2022
- posit::conf( )2 editions2023–2024
on a stage with
who talks about the same things
- Hannah Frick3 talks
- Julia Silge5 talks
- Emil Hvitfeldt4 talks
- John Mount3 talks
- Ryan Timpe3 talks
- Thomas Wiecki7 talks
| title | conference | date |
|---|---|---|
| Evaluating Time-to-Event Models is Hard | posit::conf(2024) | 2024-08-12 |
Uses tidymodels and yardstick to evaluate censored time‑to‑event models with reverse Kaplan Meier to estimate censoring probabilities and inverse probability… | ||
| Conformal Inference with Tidymodels | posit::conf(2023) | 2023-09-17 |
Code and results for using tidymodels to get prediction intervals: three concrete methods are implemented — split conformal (fit on training, compute residuals… | ||
| Good Practices for Applied Machine Learning | rstudio::conf(2022) | 2022-07-25 |
Tidymodels presents a unified R modeling stack (yardstick, tune, recipes, workflows) that encapsulates preprocessing so it applies identically at training and… | ||
| parsnip A tidy model interface | rstudio::conf 2019 | 2019-01-15 |
A new R package unifies model specification from estimation by letting users declare a model type (e.g. linear or logistic) and swap engines like glmnet, Stan… | ||
| Total Tidy Tuning Techniques | rstudio::conf 2019 | 2019-01-15 |
Ames housing data and tidymodels recipes feed into parsnip model specs where placeholders like tune() mark parameters to optimize (example: glmnet’s penalty… | ||
| Good Practices for Applied Machine Learning | rstudio::conf 2019 | 2019-01-15 |
Tidymodels is a unified R framework that packages recipes (step_date, step_normalize, genre parsing), workflows (bind a recipe to a model like Cubist), and… | ||
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