Author: Zheng Zhang; Sifan Ye; Aiping Wu; Taijiao Jiang; Yousong Peng
Title: Prediction of receptorome for human-infecting virome Document date: 2020_2_28
ID: 9ruhvpbv_23
Snippet: Five times of five-fold cross-validations were used to evaluate the predictive performances of the RF model with the function of StratifiedKFold in the package scikit-learn in Python. The predictive performances of the RF model were evaluated by the area under receiver operating characteristics curve (AUC), accuracy, sensitivity and specificity......
Document: Five times of five-fold cross-validations were used to evaluate the predictive performances of the RF model with the function of StratifiedKFold in the package scikit-learn in Python. The predictive performances of the RF model were evaluated by the area under receiver operating characteristics curve (AUC), accuracy, sensitivity and specificity.
Search related documents:
Co phrase search for related documents- area evaluate and specificity sensitivity: 1, 2, 3, 4, 5, 6
- area evaluate and specificity sensitivity accuracy: 1
- cross validation and fold cross validation: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79
- cross validation and fold cross validation time: 1
- cross validation and predictive performance: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30
- cross validation and Python learn: 1
- cross validation and RF model: 1, 2, 3, 4, 5, 6, 7
- cross validation and specificity sensitivity: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73
- cross validation and specificity sensitivity accuracy: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58
- fold cross validation and predictive performance: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14
- fold cross validation and Python learn: 1
- fold cross validation and RF model: 1, 2, 3, 4, 5, 6, 7
- fold cross validation and specificity sensitivity: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61
- fold cross validation and specificity sensitivity accuracy: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35
- fold cross validation time and specificity sensitivity: 1
- fold cross validation time and specificity sensitivity accuracy: 1
- package scikit and Python learn: 1, 2, 3, 4
- package scikit Python learn and Python learn: 1, 2, 3, 4
- predictive performance and Python learn: 1, 2
Co phrase search for related documents, hyperlinks ordered by date