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Table 3 Performance of the 5 traditional machine learning algorithms

From: Probability calibration-based prediction of recurrence rate in patients with diffuse large B-cell lymphoma

  AUC ECE MCE BS P_value
NB 0.760 (0.741–0.783) 15.711 (13.557–17.914) 34.350 (29.275–39.800) 0.212 (0.199–0.228) < 0.001(< 0.001- < 0.001)
LR 0.758 (0.733–0.779) 8.517 (7.244–10.093) 20.100 (16.675–25.025) 0.188 (0.180–0.196) 0.152 (0.022–0.403)
RF 0.757 (0.739–0.776) 12.740 (10.910–14.336) 27.200 (23.375–31.925) 0.201 (0.190–0.211) < 0.001(< 0.001- < 0.001)
SVM 0.748 (0.724–0.771) 9.872 (8.317–11.777) 23.800 (19.000–28.925) 0.194 (0.185–0.204) 0.016(< 0.001–0.117)
FFNN 0.767 (0.747–0.787) 8.238 (6.805–9.611) 20.150 (16.600–24.500) 0.184 (0.177–0.192) 0.244 (0.075–0.518)
  1. NB naïve Bayes, LR logistic regression, RF random forest, SVM support vector machine, FFNN feedforward neural network. In each cell M (P25 - P75): M is the median, P25 is the 25th percentile and P75 is the 75th percentile of 500 evaluations. The best performance in each column is in bold; The secondary best performance in each column is underlined