Figure 4
Identification of pain threshold variables that were most informative in inferring the person’ sex. (A) Sum scores of selections of each variable across the 26 different feature selection methods, sorted in decreasing order. The darker blue bars (left) indicate the d = 4 variables that had resulted from the feature selection as “reduced” feature set, with further narrowing to the "sparse” feature set of d = 2 variables (darker blue columns). (B) ABC analysis plot (blue line) showing the cumulative distribution function of the sums of occurrences in ABC category "A" in the ABC analyses previously performed with each feature selection method separately. The red lines show the boundaries between the ABC subsets "A", "B" and "C". Category "A" with d = 4 variables is considered to include the most relevant variables for class discrimination. (C) Result from a second cABC analysis performed on the results of the first analysis (recursive cABC analysis). The figure was created using Python version 3.8.13 for Linux (https://www.python.org), with the seaborn statistical data visualization package (https://seaborn.pydata.org40) and our Python package "cABCanalysis" available at https://pypi.org/project/cABCanalysis/.
