autoplot.tune_results  R Documentation 
Plot tuning search results
## S3 method for class 'tune_results'
autoplot(
object,
type = c("marginals", "parameters", "performance"),
metric = NULL,
width = NULL,
...
)
object 
A tibble of results from 
type 
A single character value. Choices are 
metric 
A character vector or 
width 
A number for the width of the confidence interval bars when

... 
For plots with a regular grid, this is passed to 
When the results of tune_grid()
are used with autoplot()
, it tries to
determine whether a regular grid was used.
For regular grids with one or more numeric tuning parameters, the parameter with the most unique values is used on the xaxis. If there are categorical parameters, the first is used to color the geometries. All other parameters are used in column faceting.
The plot has the performance metric(s) on the yaxis. If there are multiple metrics, these are rowfaceted.
If there are more than five tuning parameters, the "marginal effects" plots are used instead.
For spacefilling or random grids, a marginal effect plot is created. A panel is made for each numeric parameter so that each parameter is on the xaxis and performance is on the yxis. If there are multiple metrics, these are rowfaceted.
A single categorical parameter is shown as colors. If there are two or more
nonnumeric parameters, an error is given. A similar result occurs is only
nonnumeric parameters are in the grid. In these cases, we suggest using
collect_metrics()
and ggplot()
to create a plot that is appropriate for
the data.
If a parameter has an associated transformation associated with it (as determined by the parameter object used to create it), the plot shows the values in the transformed units (and is labeled with the transformation type).
Parameters are labeled using the labels found in the parameter object
except when an identifier was used (e.g. neighbors = tune("K")
).
A ggplot2
object.
tune_grid()
, tune_bayes()
# For grid search:
data("example_ames_knn")
# Plot the tuning parameter values versus performance
autoplot(ames_grid_search, metric = "rmse")
# For iterative search:
# Plot the tuning parameter values versus performance
autoplot(ames_iter_search, metric = "rmse", type = "marginals")
# Plot tuning parameters versus iterations
autoplot(ames_iter_search, metric = "rmse", type = "parameters")
# Plot performance over iterations
autoplot(ames_iter_search, metric = "rmse", type = "performance")
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