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Table 3 Evaluation measures of random forest (RF) and gradient boosting (GB) ensemble models

From: Prediction of plant lncRNA by ensemble machine learning classifiers

ML model type Ensemble type AUC MCC Accuracy Sensitivity Specificity
RF       
  Vote 0.834 0.725 0.944 0.594 0.995
  Arithmetic mean 0.963 0.661 0.941 0.562 0.996
  Geometric mean 0.963 0.706 0.941 0.555 0.997
  Logistic regression 0.835 0.765 0.952 0.665 0.994
GB       
  Vote 0.887 0.797 0.958 0.702 0.995
  Arithmetic mean 0.945 0.786 0.956 0.681 0.996
  Geometric mean 0.940 0.750 0.949 0.601 0.999
  Logistic regression 0.883 0.822 0.963 0.745 0.994
  1. Statistics for vote, arithmetic mean, and geometric mean models were calculated using outputs of models compared to true labels. Logistic regression evaluation statistics were calculated using the scores found by 10-fold cross validation of O. sativa training data and validated lncRNA sequences