While we've written about survival analysis and machine learning fairness already, the newest tune release includes a number of other major changes
Recent releases integrate survival analysis into tidymodels. This now unlocks the framework for censored regression and provides modeling capabilities for time-to-event data
Recent tidymodels releases integrated a set of tools for assessing whether machine learning models treat groups of people differently
The tidymodels team's biannual spring cleaning gave us a chance to revisit the way we raise some error messages
The latest releases of rsample and tune provide a new interface to validation sets as a three-way split
New releases of the tune, finetune, and workflowsets packages have made optimizing model parameters with tidymodels even more pleasant
We highlight a series of new tidymodels package versions and their improvements
finetune is a new package that adds a few more model tuning methods
With version 0.1.2 of tune, there are more options for parallel processing
Sparse data is common in many domains, and now tidymodels supports using sparse matrix structures throughout the fitting and tuning stages of modeling
A new version of the tune package contains numerous new features
The new usemodels R package is a helpful way to automatically generate tidymodels code
A new version of the tune package brings better visualizations, engine-specific parameter tuning, and other features
