ROC Curve from Performance Data — plot_roc

Plot a ROC Curve

Usage

plot_roc_curve(
  performance_data,
  chosen_threshold = NA,
  interactive = TRUE,
  color_values = c("#1b9e77", "#d95f02", "#7570b3", "#e7298a", "#07004D", "#E6AB02",
    "#FE5F55", "#54494B", "#006E90", "#BC96E6", "#52050A", "#1F271B", "#BE7C4D",
    "#63768D", "#08A045", "#320A28", "#82FF9E", "#2176FF", "#D1603D", "#585123"),
  title_included = FALSE,
  size = NULL,
  renderer = "default",
  evaluation_metadata = NULL,
  stratified_by = "probability_threshold"
)

Arguments

performance_data: an rtichoke Performance Data
chosen_threshold: a chosen threshold to display (for non-interactive)
interactive: whether the plot should be interactive plots
color_values: color palette
title_included: add title to the curve
size: the size of the curve
renderer: rendering backend. "default" preserves the existing interactive behavior; alternatives are "ggplot2", "plotly", and "browser".
evaluation_metadata: explicit semantic evaluation metadata required when renderer = "browser". It is supplied automatically by create_roc_curve().
stratified_by: Performance Metrics can be stratified by Probability Threshold or alternatively by Predicted Positives Condition Rate

Examples

if (FALSE) { # \dontrun{

one_pop_one_model |>
  plot_roc_curve()

one_pop_one_model_by_ppcr |>
  plot_roc_curve()

multiple_models |>
  plot_roc_curve()

multiple_models_by_ppcr |>
  plot_roc_curve()

multiple_populations |>
  plot_roc_curve()

multiple_populations_by_ppcr |>
  plot_roc_curve()
} # }