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
        """
        X = df_features.values
        y = df_target.values
        regressor = DecisionTreeRegressor()
        regressor.fit(X, y)

        return regressor.feature_importances_
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
        """
        X = df_features.values
        y = df_target.values
        regressor = DecisionTreeRegressor()
        regressor.fit(X, y)

        return regressor.feature_importances_
def predict_features ( self , df_features , df_target , idx = 0 , * * kwargs ) : X = df_features . values y = df_target . values regressor = DecisionTreeRegressor ( ) regressor . fit ( X , y ) return regressor . feature_importances_
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 .