Selected article for: "accuracy f1 score and lr logistic regression"
Author: Rahul Kumar; Ridhi Arora; Vipul Bansal; Vinodh J Sahayasheela; Himanshu Buckchash; Javed Imran; Narayanan Narayanan; Ganesh N Pandian; Balasubramanian Raman
Title: Accurate Prediction of COVID-19 using Chest X-Ray Images through Deep Feature Learning model with SMOTE and Machine Learning Classifiers
  • Document date: 2020_4_17
    • ID: 59ghorzf_10
    Snippet: The corresponding performance metrics of RF and XGB in terms of Accuracy, Sensitivity, Specificity, F1-Score, and AUC are shown in Table 2 . It seems that XGBoost performed the best among all the classifiers with an accuracy of 97.7%, while Logistic Regression (LR), k-Nearest Neighbour (kNN),.....
    Document: The corresponding performance metrics of RF and XGB in terms of Accuracy, Sensitivity, Specificity, F1-Score, and AUC are shown in Table 2 . It seems that XGBoost performed the best among all the classifiers with an accuracy of 97.7%, while Logistic Regression (LR), k-Nearest Neighbour (kNN),

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