def predict_features(self, df_features, df_target, idx=0, **kwargs):
        """For one variable, predict its neighbouring nodes.

        Args:
            df_features (pandas.DataFrame):
            df_target (pandas.Series):
            idx (int): (optional) for printing purposes
            kwargs (dict): additional options for algorithms

        Returns:
            list: scores of each feature relatively to the target
        """
        estimator = SVR(kernel='linear')
        selector = RFECV(estimator, step=1)
        selector = selector.fit(df_features.values, df_target.values[:, 0])

        return selector.grid_scores_
def predict_features(self, df_features, df_target, idx=0, **kwargs):
        """For one variable, predict its neighbouring nodes.

        Args:
            df_features (pandas.DataFrame):
            df_target (pandas.Series):
            idx (int): (optional) for printing purposes
            kwargs (dict): additional options for algorithms

        Returns:
            list: scores of each feature relatively to the target
        """
        estimator = SVR(kernel='linear')
        selector = RFECV(estimator, step=1)
        selector = selector.fit(df_features.values, df_target.values[:, 0])

        return selector.grid_scores_
def predict_features ( self , df_features , df_target , idx = 0 , * * kwargs ) : estimator = SVR ( kernel = 'linear' ) selector = RFECV ( estimator , step = 1 ) selector = selector . fit ( df_features . values , df_target . values [ : , 0 ] ) return selector . grid_scores_
For one variable, predict its neighbouring nodes.

        Args:
            df_features (pandas.DataFrame):
            df_target (pandas.Series):
            idx (int): (optional) for printing purposes
            kwargs (dict): additional options for algorithms

        Returns:
            list: scores of each feature relatively to the target
For one variable predict its neighbouring nodes .