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Bite-sized tricks for machine learning with tidymodels | Posit
The tidymodels framework is a collection of R packages for modeling and machine learning using tidyverse principles. This video highlights a number of tidymodels features that could improve your modeling workflows.
0:03 Switching modeling engines is easy
0:21 Never lose your tuning results
0:36 Built-in visualizations for modeling objects
1:03 Grouped resampling
1:16 Case weights
1:32 Select variables based on role and type
2:00 Spatial resampling
2:16 Keep your tidymodels objects small
Learn more at https://www.tidymodels.org/
0:03 Switching modeling engines is easy
0:21 Never lose your tuning results
0:36 Built-in visualizations for modeling objects
1:03 Grouped resampling
1:16 Case weights
1:32 Select variables based on role and type
2:00 Spatial resampling
2:16 Keep your tidymodels objects small
Learn more at https://www.tidymodels.org/
Transcript#
This transcript was generated automatically and may contain errors.
Tags:Rstudio,Data Science,Machine Learning,Python,Stats,Tidyverse,Data Visualization,Data Viz,Ggplot,Technology,Coding,Connect,Server Pro,Shiny,RMarkdown,Package Manager,CRAN,Interoperability,Serious Data Science,Dplyr,Forcats,Ggplot2,Tibble,Readr,Stringr,Tidyr,Purrr,Github,Data Wrangling,Tidy Data,Odbc,Rayshader,Plumber,Blogdown,Gt,Lazy Evaluation,Tidymodels,Statistics,Debugging,Programming Education,Rstats,Open Source,OSS,Reticulate
