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Extending a tinyAML and shiny App

Steven Sanderson wraps up a series on shiny and tinyAML. Part 3 extends options for regression:

As data science continues to be a sought-after field, creating a reliable and accurate model is essential. While there are various machine learning algorithms available, the process of selecting the correct algorithm can be complex. The {tidyAML} package, part of the tidymodels suite, offers an easy-to-use, consistent interface for building machine learning models. In this post, we will explore a Shiny application that utilizes tidyAML to build a machine learning model.

Today I have updated the tidyAML shiny app to include the ability to set the parameter of the fast_regression() function .parsnip_fns and this is things like linear_reg.

And part 4 includes classification:

This is a Shiny app for building models using the {tidyAML} which is based on the tidymodels package in R. The app allows you to upload your own data or choose from one of two built-in datasets (mtcars or iris) and select the type of model you want to build (regression or classification).

Let’s take a closer look at the code.

This was an interesting series, for sure.