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
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.
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
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
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.
Many of the output columns in a
tune_results object have an additional column called
.config. This is meant to be a unique, qualitative value that used for sorting and merging. These values also correspond to the messages in the logging produced when
verbose = TRUE.
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.