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Table 5 Classification experimental results of different machine learning classifiers

From: Efficacy prediction of noninvasive ventilation failure based on the stacking ensemble algorithm and autoencoder

Classifiers Accuracy AUC F1 Precision Recall
Random forest 0.862 0.896 0.729 0.861 0.633
Logistic regression 0.834 0.905 0.758 0.663 0.884
Catboost 0.866 0.901 0.755 0.817 0.701
SVM 0.832 0.845 0.754 0.662 0.878
GBDT 0.862 0.892 0.745 0.815 0.687
XGBoost 0.858 0.888 0.732 0.822 0.66
LightGBM 0.872 0.893 0.763 0.837 0.701
SMSN 0.882 0.915 0.794 0.814 0.776