Selected article for: "model performance and random forest"
Author: Yujia Xiang; Quan Zou; Lilin Zhao
Title: VPTMdb: a viral post-translational modification database
  • Document date: 2020_4_2
    • ID: kl99afiu_67
    Snippet: To assess the robustness and performance of the svm, random forest and naïve Bayes in 68D features, 10-fold random independent tests were performed. The model performance on independent datasets is shown in Figure 4 , random forest performed better, the average AUC, MCC, F1score of its are 0.744, 0.427, 0.656 respectively. Comparing random forest and PSI-blast (Supplementary Table S4 ), the MCC, acc and sp values of random forest are higher than.....
    Document: To assess the robustness and performance of the svm, random forest and naïve Bayes in 68D features, 10-fold random independent tests were performed. The model performance on independent datasets is shown in Figure 4 , random forest performed better, the average AUC, MCC, F1score of its are 0.744, 0.427, 0.656 respectively. Comparing random forest and PSI-blast (Supplementary Table S4 ), the MCC, acc and sp values of random forest are higher than PSI-blast for 6.92%, 2.8% and 19.1%. Taking all indicators into consideration, our method is stable and better performance. We implemented svm, random forest and naïve Bayes into VPTMpre, users can choose them to predict phosphorylated sites of interest.

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