NEWS.md
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.
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_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).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()