Return target value predictions for ``dataset``, using the trained
        linear regression model. This method can be used to get fitted values
        for the model by inputting the training dataset.

        Parameters
        ----------
        dataset : SFrame | pandas.Dataframe
            Dataset of new observations. Must include columns with the same
            names as the features used for model training, but does not require
            a target column. Additional columns are ignored.

        missing_value_action : str, optional
            Action to perform when missing values are encountered. This can be
            one of:

            - 'auto': Default to 'impute'
            - 'impute': Proceed with evaluation by filling in the missing
              values with the mean of the training data. Missing
              values are also imputed if an entire column of data is
              missing during evaluation.
            - 'error': Do not proceed with prediction and terminate with
              an error message.


        Returns
        -------
        out : SArray
            Predicted target value for each example (i.e. row) in the dataset.

        See Also
        ----------
        create, evaluate

        Examples
        ----------
        >>> data =  turicreate.SFrame('https://static.turi.com/datasets/regression/houses.csv')

        >>> model = turicreate.linear_regression.create(data,
                                             target='price',
                                             features=['bath', 'bedroom', 'size'])
        >>> results = model.predict(data)

def predict(self, dataset, missing_value_action='auto'):
        """
        Return target value predictions for ``dataset``, using the trained
        linear regression model. This method can be used to get fitted values
        for the model by inputting the training dataset.

        Parameters
        ----------
        dataset : SFrame | pandas.Dataframe
            Dataset of new observations. Must include columns with the same
            names as the features used for model training, but does not require
            a target column. Additional columns are ignored.

        missing_value_action : str, optional
            Action to perform when missing values are encountered. This can be
            one of:

            - 'auto': Default to 'impute'
            - 'impute': Proceed with evaluation by filling in the missing
              values with the mean of the training data. Missing
              values are also imputed if an entire column of data is
              missing during evaluation.
            - 'error': Do not proceed with prediction and terminate with
              an error message.


        Returns
        -------
        out : SArray
            Predicted target value for each example (i.e. row) in the dataset.

        See Also
        ----------
        create, evaluate

        Examples
        ----------
        >>> data =  turicreate.SFrame('https://static.turi.com/datasets/regression/houses.csv')

        >>> model = turicreate.linear_regression.create(data,
                                             target='price',
                                             features=['bath', 'bedroom', 'size'])
        >>> results = model.predict(data)
        """

        return super(LinearRegression, self).predict(dataset, missing_value_action=missing_value_action)