Performance curves

Author

Cox Lab

Published

March 18, 2024

1 General

2 Brief description

Calculation of predictive performance measures like precision-recall or ROC curves.

#Parameters

2.1 Indicated are

Specification whether rows containing the “Indicator” in the categorical column specified in “In column” correspond to the class under observation or not (default: False).

2.2 In column

Selected categorical column containing the class membership of each instance (row) of the class under observation (default: first categorical column in the matrix).

2.3 Indicator

Rows containing the defined string are counted as true or false depending on the selection in “Indicated are” (default: \(+\)).

2.4 Scores

Selected expression columns containing the scores by which the rows are ranked to calculate the specified quantities (default: first expression column of the matrix is selected).

2.5 Large values are good

If checked the larger the score value the better (default: checked). Otherwise the lower the value the better.

2.6 Display quantity

Selected quantities that will be calculated (default: no quantities are selected). The quantities can be selected from a predefined list:

  • \(TP/(TP+FP)\) (Precision)
  • \(TP/(TP+FN)\) (Recall)
  • \(FP/TP\)
  • \(TP/NP\)
  • \(TP/(TP+FN)\) (Sensitivity)
  • \(TN/(TN+FP)\) (Specificity)

3 Parameter window

Perseus pop-up window: Basic -> Performance curve