Selected article for: "logistic regression and model performance"
Author: Zhao, Chun-Hong; Wu, Hui-Tao; Che, He-Bin; Song, Ya-Nan; Zhao, Yu-Zhuo; Li, Kai-Yuan; Xiao, Hong-Ju; Zhai, Yong-Zhi; Liu, Xin; Lu, Hong-Xi; Li, Tan-Shi
Title: Prediction of fatal adverse prognosis in patients with fever-related diseases based on machine learning: A retrospective study
  • Document date: 2020_3_5
    • ID: tk3861u0_16
    Snippet: The coefficients and OR values of the variables in the logistic regression are shown in Table 3 To explore the most important factors, we reduced the optimal number of RFE to 11 and repeated the above process. Similarly, the bagging model showed the best performance on sensitivity and ROC-AUC, while the logistic regression showed the best accuracy. The details are shown in Table 2 Table 2 ......
    Document: The coefficients and OR values of the variables in the logistic regression are shown in Table 3 To explore the most important factors, we reduced the optimal number of RFE to 11 and repeated the above process. Similarly, the bagging model showed the best performance on sensitivity and ROC-AUC, while the logistic regression showed the best accuracy. The details are shown in Table 2 Table 2 .

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