DecisionTree scores

The datasets presented in this repository are obtained by applying the decision tree (<i>DecisionTree</i>) 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>".Hyperparametersmax_features: sqrtmax_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 (n_split, max_features, 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>