SVC scores (linear kernel and C=1)

The datasets presented in this repository are obtained by applying the support vector classifier (SVC) algorithm with the following specific hyperparameter setting and the different inputs that have been described in the journal paper: "<b>Data mining techniques for endometriosis detection in a data-scarce medical dataset</b>".HyperparametersC: 1.0kernel: linearmax_iter: 1000000Files<b>result_eb.csv</b>: Results for EB sample type.<b>result_ef.csv</b>: Results for EF sample type.<b>result_vagina.csv</b>: Results for vagina sample type.<b>result_oral.csv</b>: Results for oral sample type.<b>result_feces.csv</b>: Results for feces sample type.<b>result_frt.csv</b>: Results for FRT (EB + EF + vagina) sample type.<b>result_frt2.csv</b>: Results for FRT2 (EB + vagina) sample type.<b>properties_eb.log</b>: Arguments and result information for EB sample type (C, n_split, kernel, gamma, max_iter, random_state, len_scores_before_filtering, len_scores_after_filtering, len_f1).<b>properties_ef.log</b>: Arguments and result information for EF sample type.<b>properties_vagina.log</b>: Arguments and result information for vagina sample type.<b>properties_oral.log</b>: Arguments and result information for oral sample type.<b>properties_feces.log</b>: Arguments and result information for feces sample type.<b>properties_frt.log</b>: Arguments and result information for FRT sample type.<b>properties_frt2.log</b>: Arguments and result information for FRT2 sample type.<br>