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Table 7 Mean scores on the Agincourt dataset

From: Automatically determining cause of death from verbal autopsy narratives

  Precision Sensitivity F 1 PCCC CSMFA CCCSMFA
Adult (15–69 years)
Naïve Bayes .517 .517 .513 .481 .932 .814
Random forest .511 .517 .496 .480 .844 .577
SVM .569 .566 .561 .543 .901 .730
Neural network .575 .578 .570 .547 .918 .777
Child (29 days–14 years)
Naïve Bayes .488 .440 .435 .395 .761 .351
Random forest .521 .502 .487 .463 .816 .501
SVM .535 .518 .512 .479 .872 .653
Neural network .572 .562 .552 .527 .869 .645
Neonate (<29 days)
Naïve Bayes .532 .526 .483 .404 .702 .191
Random forest .409 .496 .427 .366 .710 .213
SVM .387 .417 .371 .266 .693 .165
Neural network .356 .412 .354 .259 .636 .012
  1. CCCSMFA was calculated using.632 as the mean of random allocation, as suggested in [12]