code: def get_best(self):
          
          svc = SVC(kernel="linear")
          rfecv = RFECV(
              estimator=svc,
              step=1,
              cv=StratifiedKFold(self.y_train, 2),
              scoring="log_loss"
          )
          rfecv.fit(self.x_train, self.y_train)
          return rfecv.n_features_, rfecv.ranking_
docs: Finds the optimal number of features
          :return: optimal number of features and ranking