plot_metric: Plot Stored Metrics Across Model Resamples

In rtemis: Schema-Driven Machine Learning and Visualization

Plot Stored Metrics Across Model Resamples

Description

S7 generic for SupervisedRes results, including regression and classification. Uses recorded per-resample metrics in resample_ids order; list names do not change that producer contract. The default metric is balanced_accuracy for classification and rsq for regression. Classification selects overall metrics, not per-class scores.

Usage

plot_metric(x, ...)

Arguments

x	rtemis::SupervisedRes: Resampled fitted model.
...	Method arguments, including what (Character: "all" or unique "training" and/or "test" samples) and metric (Optional Character: stored metric name), followed by additional arguments to rtemis.draw::draw_metric().

Details

All available training and test reports are selected by default. An explicitly requested unavailable report is an error. Missing metric values remain missing; a metric absent from every selected report is an error. No predictions or aggregate means are used to reconstruct fold values.

Value

htmlwidget: Metric distributions with all observations by default.

Examples

model <- train(mtcars[, c("wt", "mpg")],
  hyperparameters = setup_GLM(),
  outer_resampling_config = setup_KFold(n_resamples = 3L),
  execution_config = setup_SerialExecution(),
  verbosity = 0L
)
plot_metric(model)

rtemis documentation built on Oct. 20, 2026, 1:06 a.m.