Selected article for: "random forest and training validation"
Author: Yujia Xiang; Quan Zou; Lilin Zhao
Title: VPTMdb: a viral post-translational modification database
  • Document date: 2020_4_2
    • ID: kl99afiu_45
    Snippet: In this work, six descriptors and their combined features' performance were compared using 5-fold cross validation in the training data with the Support Vector Machine (SVM) method. Subsequently, the Minimum redundancy and maximum relevance (mRMR) method was chosen to select the most meaningful features. To investigate the predictive performance of three classifiers, we compared the different dimensions of features in the svm, random forest, naï.....
    Document: In this work, six descriptors and their combined features' performance were compared using 5-fold cross validation in the training data with the Support Vector Machine (SVM) method. Subsequently, the Minimum redundancy and maximum relevance (mRMR) method was chosen to select the most meaningful features. To investigate the predictive performance of three classifiers, we compared the different dimensions of features in the svm, random forest, naïve Bayes methods. The features that performed well in all three classifiers were selected as the most meaningful and significant features. The T-distributed Stochastic Neighbour Embedding algorithm was used to visualize the features(van der Maaten and Hinton, 2008).

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