Evaluating multiple classifiers after SVM-RFE (Support Vector Machine-Recursive Feature Elimination)
After SVM-RFE optimization and selecting the most important features (peptide-centric), following classifiers were trained using Monter-Carlo Cross-Validation (#100 iterations) and reported the accuracy score from validation dataset;
Random Forest
XGBoost
ExtraTree
Logistic Regression (L1, Ridge regression)
Logistic Regression (L2, LASSO)
SVM (Linear Kernel)
SVM (RBF Kernel)
Gaussian Naive Bayes
Bagging
Test accuracy results can be seen by PDF file.