Plot prediction intervals and their coverage

Observations are sorted by prediction and drawn with their conformal interval, with the ones the interval missed picked out. A calibrated interval should miss about 1 - level of them, scattered rather than concentrated at one end.

Usage

plot_uncertainty(model, level = 0.9, ...)

Arguments

model

A model fitted by train_model().

level

Coverage level.

...

Passed to plot.

Value

A data frame of predictions, bounds and whether each was covered, invisibly.

See also

uncertainty(), predict.agri_model()

Examples

p <- agri_project(demo_agri_data(n_units = 20, n_seasons = 3))
p <- build_features(phenology_windows(add_climate(p)), stats = "sum")
m <- train_model(p, "yield", algorithm = "lm", k = 3)
plot_uncertainty(m)