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This function creates a plot representing multiple evaluations of a learning method across different training-set sizes.

Usage

plot_multi_evaluation(
  data,
  thr = NULL,
  add.uncertainty = T,
  add.boxplot = T,
  add.scores = T,
  add.best = T,
  shape.best = 22,
  size.best = 3,
  scale.x = F,
  title = "Evaluation",
  subtitle = ggplot2::waiver(),
  caption = ggplot2::waiver(),
  xlab = "Training-set size",
  ylab = "Performance",
  ...
)

Arguments

data

data.frame containing the data to plot. The function expects specific columns:

training_set_size

contains the considered training-set sizes

score

contains the performance metric for each model

mean_score

contains the mean performance metric for the specific training-set size

lower_ci

contains the lower bound of the confidence interval for the mean score

upper_ci

contains the upper bound of the confidence interval for the mean score

best_resample

contains the index of the automatically selected optimal training-set size

best_model

contains the index of the best model for the optimal training-set size

name

contains a grouping key, e.g. the learning method

thr

numerical value, if provided it is used to draw an horizontal line

add.uncertainty

logical, whether to include the quantified uncertainty of the performance estimate in the plot

add.boxplot

logical, whether to include a boxplot in the figure

add.scores

logical, whether to add the performance metric of individual models as points in the plot

add.best

logical, whether to add a point indicating the performance of what is reported as best model in data

shape.best

integer, shape aesthetic passed to geom_point

size.best

integer, size aesthetic passed to geom_point

scale.x

logical, whether to force the scaling of the x-axis

title

character string, the title of the plot

subtitle

character string, the subtitle of the plot

caption

character string, the caption of the plot

xlab, ylab

character string, axes labels

...

further arguments to ggplot

Value

A ggplot object

Details

A plot showing the mean performance and the related 95\ across different training-set sizes is produced. Individual scores and summary metrics in the form of boxplots can be also added (default) via the add.scores and add.boxplot arguments, respectively.

Author

Alessandro Barberis