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
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
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_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.
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_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.