workflowsets (version 1.1.0)

autoplot.workflow_set: Plot the results of a workflow set

Description

This autoplot() method plots performance metrics that have been ranked using a metric. It can also run autoplot() on the individual results (per wflow_id).

Usage

# S3 method for workflow_set
autoplot(
  object,
  rank_metric = NULL,
  metric = NULL,
  id = "workflow_set",
  select_best = FALSE,
  std_errs = qnorm(0.95),
  type = "class",
  ...
)

Value

A ggplot object.

Arguments

object

A workflow_set whose elements have results.

rank_metric

A character string for which metric should be used to rank the results. If none is given, the first metric in the metric set is used (after filtering by the metric option).

metric

A character vector for which metrics (apart from rank_metric) to be included in the visualization.

id

A character string for what to plot. If a value of "workflow_set" is used, the results of each model (and sub-model) are ordered and plotted. Alternatively, a value of the workflow set's wflow_id can be given and the autoplot() method is executed on that workflow's results.

select_best

A logical; should the results only contain the numerically best submodel per workflow?

std_errs

The number of standard errors to plot (if the standard error exists).

type

The aesthetics with which to differentiate workflows. The default "class" maps color to the model type and shape to the preprocessor type. The "workflow" option maps a color to each "wflow_id". This argument is ignored for values of id other than "workflow_set".

...

Other options to pass to autoplot().

Details

This function is intended to produce a default plot to visualize helpful information across all possible applications of a workflow set. A more appropriate plot for your specific analysis can be created by calling rank_results() and using standard ggplot2 code for plotting.

The x-axis is the workflow rank in the set (a value of one being the best) versus the performance metric(s) on the y-axis. With multiple metrics, there will be facets for each metric.

If multiple resamples are used, confidence bounds are shown for each result (90% confidence, by default).

Examples

autoplot(two_class_res)
autoplot(two_class_res, select_best = TRUE)
autoplot(two_class_res, id = "yj_trans_cart", metric = "roc_auc")