Selected article for: "method performance and training dataset"
Author: Kshirsagar, Meghana; Carbonell, Jaime; Klein-Seetharaman, Judith
Title: Multitask learning for host–pathogen protein interactions
  • Document date: 2013_7_1
    • ID: sdgt2ms5_79
    Snippet: The F1 measure gave us a quantitative idea of the performance of each method on training data. In this section, we present a qualitative analysis of the new interactions that our models predict. We first construct, for each task 'T t ', a random set R t of protein pairs that is disjoint from the training dataset. We train the pairwise models on the training data and obtain predictions on R t . The method described in Section 4.3 is used to aggreg.....
    Document: The F1 measure gave us a quantitative idea of the performance of each method on training data. In this section, we present a qualitative analysis of the new interactions that our models predict. We first construct, for each task 'T t ', a random set R t of protein pairs that is disjoint from the training dataset. We train the pairwise models on the training data and obtain predictions on R t . The method described in Section 4.3 is used to aggregate predictions from all pairwise models. The subset of R t labeled as 'positive' is used for the analysis described below. i224

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