plot_probregr: Visualise probabilistic regression distribution predictions

In mlr-org/mlr3proba: Probabilistic Supervised Learning for 'mlr3'

Visualise probabilistic regression distribution predictions

Description

Plots probability density functions from n predicted probability distributions.

Usage

plot_probregr(
  p,
  n,
  type = c("point", "line", "both", "none"),
  which_plot = c("random", "top"),
  rm_zero = TRUE,
  ...
)

Arguments

p	(PredictionRegr) With at least column distr.
n	(integer(1)) Number of predictions to plot.
type	(character(1)) One of "point" (default), "line", "both", "none".
which_plot	(character(1)) One of "random" (default) or "top". See details.
rm_zero	(logical(1)) If TRUE (default) does not plot points where f(x) = 0.
...	Unused

Details

type:

• "point" (default) - Truth plotted as point (truth, predicted_pdf(truth))

• "line" - Truth plotted as vertical line intercepting x-axis at the truth.

• "both" - Plots both the above options.

• "none" - Truth not plotted (default if p$truth is missing).

which_plot:

• "random"⁠(default) - Random selection of⁠n' distributions are plotted.

• "top"- Topn' distributions are plotted.

It is unlikely the plot will be interpretable when ⁠n >> 5⁠.

Examples

## Not run: 
library(mlr3verse)
task = tsk("boston_housing")
pipe = as_learner(ppl("probregr", lrn("regr.ranger"), dist = "Normal"))
p = pipe$train(task)$predict(task)
plot_probregr(p, 10, "point", "top")

## End(Not run)

mlr-org/mlr3proba documentation built on April 12, 2025, 4:38 p.m.