autoplot.tune_results() now 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.
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.
The outcome names are saved in an attribute called outcomes to objects with class tune_results. Also, several accessor functions (named `.get_tune_*()) were added to more easily access such attributes.
conf_mat_resampled() computes the average confusion matrix across resampling statistics for a single model.
show_best(), and the select_*() functions will now use the first metric in the metric set if no metric is supplied.
filter_parameters() can trim the .metrics column of unwanted results (as well as columns .predictions and .extracts) from tune_* objects.
In concert with dials > 0.0.7, tuning engine-specific arguments is possible. Many known engine-specific tuning parameters and handled automatically.
If a grid is given, parameters do not need to be finalized to be used in the tune_*() functions.
Added a save_workflow argument to control_* functions that will result in the workflow object used to carry out tuning/fitting (regardless of whether a formula or recipe was given as input to the function) to be appended to the resulting tune_results object in a workflow attribute. The new .get_tune_workflow() function can be used to access the workflow.
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.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.
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).CRAN release.
Changed license to MIT
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()
NEWS.md file to track changes to the package.