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Table 4 Performance of machine learning for two-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 0.760 0.776 0.805 0.748 0.810 0.764 0.770 0.784 0.753 0.796
Recall 0.828 0.843 0.859 0.816 0.861 0.831 0.837 0.847 0.821 0.854
Precision 0.824 0.841 0.867 0.813 0.870 0.828 0.833 0.850 0.818 0.860
F1 Score 0.821 0.841 0.862 0.808 0.864 0.823 0.832 0.848 0.813 0.855
Hamming 0.172 0.157 0.141 0.184 0.139 0.169 0.163 0.153 0.179 0.146
Jaccard 0.707 0.738 0.768 0.690 0.772 0.710 0.724 0.748 0.697 0.759
Kappa 0.554 0.564 0.583 0.532 0.586 0.564 0.565 0.560 0.542 0.573
  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