Selected article for: "bias risk and high risk"
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_21
    Snippet: The random-forest (RF) model is an ensemble machine learning technique using multiple decision trees and can handle data with high variance and high bias, while the risk of over-fitting can be significantly reduced by averaging multiple trees. Therefore, we chose the RF model to distinguish human virus receptors from other human cell membrane proteins. Because the number of positive samples, i.e., human viral author/funder. All rights reserved. N.....
    Document: The random-forest (RF) model is an ensemble machine learning technique using multiple decision trees and can handle data with high variance and high bias, while the risk of over-fitting can be significantly reduced by averaging multiple trees. Therefore, we chose the RF model to distinguish human virus receptors from other human cell membrane proteins. Because the number of positive samples, i.e., human viral author/funder. All rights reserved. No reuse allowed without permission.

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