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Table 3 Performance of machine learning for three-class task prediction

From: Interpretable classifiers for prediction of disability trajectories using a nationwide longitudinal database

  Full variables Selected variables with LASSO
LR SVM RF ANN XGBoost LR SVM RF ANN XGBoost
Accuracy 0.706 0.734 0.773 0.735 0.771 0.714 0.744 0.774 0.744 0.771
Recall 0.779 0.810 0.844 0.785 0.834 0.780 0.810 0.843 0.780 0.840
Precision 0.769 0.807 0.854 0.775 0.833 0.771 0.800 0.848 0.773 0.848
F1 Score 0.760 0.807 0.848 0.766 0.832 0.761 0.802 0.845 0.759 0.843
Hamming 0.221 0.190 0.156 0.215 0.166 0.220 0.190 0.157 0.220 0.160
Jaccard 0.639 0.705 0.759 0.645 0.735 0.639 0.696 0.754 0.636 0.751
Kappa 0.507 0.495 0.568 0.516 0.567 0.512 0.518 0.575 0.514 0.564
  1. Accuracy, recall, precision, F1 score were all calculated with weighted metrics. Hamming, Jaccard, and Kappa refer to Hamming distance, Jaccard similarity coefficient, and Cohen’s kappa score