Plot confusion matrix

Usage

draw_confusion(
  x,
  xlab = "Predicted",
  ylab = "Reference",
  true_col = "#72CDF4",
  false_col = "#FEB2E0",
  font_size = 18,
  main = NULL,
  main_y = 1,
  main_yanchor = "bottom",
  theme = rtemis_theme,
  margin = list(l = 20, r = 5, b = 5, t = 20),
  filename = NULL,
  file_width = 500,
  file_height = 500,
  file_scale = 1,
  ...
)

Arguments

x: ClassificationMetrics object produced by classification_metrics or confusion matrix where rows are the reference and columns are the estimated classes. For binary classification, the first row and column are the positive class.
xlab: Character: x-axis label. Default is "Predicted".
ylab: Character: y-axis label. Default is "Reference".
true_col: Color for true positives & true negatives.
false_col: Color for false positives & false negatives.
font_size: Integer: font size.
main: Character: plot title.
main_y: Numeric: y position of the title.
main_yanchor: Character: y anchor of the title.
theme: List or Character: Either the output of a theme_*() function or the name of a theme. Use themes() to get available theme names. Theme functions are of the form theme_<name>.
margin: List: Plot margins.
filename: Character: file name to save the plot. Default is NULL.
file_width: Numeric: width of the file. Default is 500.
file_height: Numeric: height of the file. Default is 500.
file_scale: Numeric: scale of the file. Default is 1.
...: Additional arguments passed to theme functions.

Value

A plotly object.

Author

EDG

Examples

if (FALSE) { # \dontrun{
# Assume positive class is "b"
true_labels <- factor(c("a", "a", "a", "b", "b", "b", "b", "b", "b", "b"))
predicted_labels <- factor(c("a", "b", "a", "b", "b", "a", "b", "b", "b", "a"))
predicted_prob <- c(0.3, 0.55, 0.45, 0.75, 0.57, 0.3, 0.8, 0.63, 0.62, 0.39)
metrics <- classification_metrics(true_labels, predicted_labels, predicted_prob)
draw_confusion(metrics)
} # }