Finds the optimal number of features
        :return: optimal number of features and ranking

def get_best(self):
        """Finds the optimal number of features
        :return: optimal number of features and ranking
        """
        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_