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Plot training and testing performance boxplots of multiple Supervised or SupervisedRes objects

Usage

present.list(
  x,
  metric = NULL,
  model_names = NULL,
  ylim = NULL,
  theme = choose_theme(getOption("rtemis_theme")),
  boxpoints = "all",
  filename = NULL,
  file_width = 800,
  file_height = 600,
  file_scale = 1,
  verbosity = 1L
)

Arguments

x

List of Supervised or SupervisedRes objects.

metric

Character: Metric to plot.

model_names

Character: Names of models being plotted.

ylim

Numeric vector of length 2: y-axis limits for the boxplots.

theme

Theme object.

boxpoints

Character: "all", "outliers", or "suspectedoutliers". Determines how points are displayed in the boxplot.

filename

Character: Filename to save the plot to.

file_width

Numeric: Width of the exported image in pixels.

file_height

Numeric: Height of the exported image in pixels.

file_scale

Numeric: Scale factor for the exported image.

verbosity

Integer: Verbosity level.

Value

plotly object

Author

EDG

Examples

if (FALSE) { # \dontrun{
iris_lightrf <- train(
  iris,
  algorithm = "lightrf",
  outer_resampling_config = setup_Resampler(seed = 2026)
)
iris_rsvm <- train(
  iris,
  algorithm = "radialsvm",
  outer_resampling_config = setup_Resampler(seed = 2026)
)
present(list(iris_lightrf, iris_rsvm), metric = "Balanced_Accuracy")
} # }