tune Unreleased

Breaking Changes

  • autoplot.tune_results() not requires objects made by version 0.1.0 or higher of tune.

  • tune objects no longer keep the rset class that they have from the resamples argument.

Other Changes

  • autoplot.tune_results() now produces a different plot when the tuning grid is a regular grid (i.e. factorial or nearly factorial in nature). If there are 5+ parameters, the standard plot is produced. Non-regular grids are plotted in the same way (although see next bullet point). See ?autoplot.tune_results for more information.

  • autoplot.tune_results() now transforms the parameter values for the plot. For example, if the penalty parameter was used for a regularized regression, the points are plotted on the log-10 scale (its default transformation). For non-regular grids, the facet labels show the transformation type (e.g. "penalty (log-10)" or "cost (log-2)"). For regular grid, the x-axis is scaled using scale_x_continuous().

  • Finally, autoplot.tune_results() now shows the parameter labels in a plot. For example, if a k-nearest neighbors model was used with neighbors = tune(), the parameter will be labeled as "# Nearest Neighbors". When an ID was used, such as neighbors = tune("K"), this is used to identify the parameter.

  • In other plotting news, coord_obs_pred() has been included for regression models. When plotting the observed and predicted values from a model, this forces the x- and y-axis to be the same range and uses an aspect ratio of 1.

tune 0.1.0 2020-04-02

Breaking Changes

  • The arguments to the main tuning/fitting functions (tune_grid(), tune_bayes(), etc) have been reordered to better align with parsnip’s fit(). The first argument to all these functions is now a model specification or model workflow. The previous versions are soft-deprecated as of 0.1.0 and will be deprecated as of 0.1.2.

Other Changes

  • Added more packages to be fully loaded in the workers when run in parallel using doParallel (#157), (#159), and (#160)

  • collect_predictions() gains two new arguments. parameters allows for pre-filtering of the hold-out predictions by tuning parameters values. If you are only interested in one sub-model, this makes things much faster. The other option is summarize and is used when the resampling method has training set rows that are predicted in multiple holdout sets.

  • select_best(), select_by_one_std_err(), and select_by_pct_loss() no longer have a redundant maximize argument (#176). Each metric set in yardstick now has a direction (maximize vs. minimize) built in.

Bug Fixes

  • tune_bayes() no longer errors with a recipe, which has tuning parameters, in combination with a parameter set, where the defaults contain unknown values (#168).

tune 0.0.1 2020-02-11

  • CRAN release.

  • Changed license to MIT

tune Unreleased

  • The ... arguments of tune_grid() and tune_bayes() have been moved forward to force optional arguments to be named.

  • New fit_resamples() for fitting a set of resamples that don’t require any tuning.

  • Changed summarise.tune_results() back to estimate.tune_results()

tune Unreleased

  • Added a NEWS.md file to track changes to the package.