tabular/src/autogluon/tabular/task/tabular_prediction/predictor.py
def predict(self, dataset, model=None, as_pandas=False):
        """ Use trained models to produce predicted labels (in classification) or response values (in regression).

            Parameters
            ----------
            dataset : str or :class:`TabularDataset` or `pandas.DataFrame`
                The dataset to make predictions for. Should contain same column names as training Dataset and follow same format
                (may contain extra columns that won't be used by Predictor, including the label-column itself).
                If str is passed, `dataset` will be loaded using the str value as the file path.
            model : str (optional)
                The name of the model to get predictions from. Defaults to None, which uses the highest scoring model on the validation set.
                Valid models are listed in this `predictor` by calling `predictor.get_model_names()`
            as_pandas : bool (optional)
                Whether to return the output as a pandas Series (True) or numpy array (False)

            Returns
            -------
            Array of predictions, one corresponding to each row in given dataset. Either numpy Ndarray or pandas Series depending on `as_pandas` argument.

        """
        dataset = self.__get_dataset(dataset)
        return self._learner.predict(X=dataset, model=model, as_pandas=as_pandas)
 Use trained models to produce predicted labels (in classification) or response values (in regression).

            Parameters
            ----------
            dataset : str or :class:`TabularDataset` or `pandas.DataFrame`
                The dataset to make predictions for. Should contain same column names as training Dataset and follow same format
                (may contain extra columns that won't be used by Predictor, including the label-column itself).
                If str is passed, `dataset` will be loaded using the str value as the file path.
            model : str (optional)
                The name of the model to get predictions from. Defaults to None, which uses the highest scoring model on the validation set.
                Valid models are listed in this `predictor` by calling `predictor.get_model_names()`
            as_pandas : bool (optional)
                Whether to return the output as a pandas Series (True) or numpy array (False)

            Returns
            -------
            Array of predictions, one corresponding to each row in given dataset. Either numpy Ndarray or pandas Series depending on `as_pandas` argument.

        
Use trained models to produce predicted labels (in classification) or response values (in regression).
Parameters

dataset : str or :class:`TabularDataset` or `pandas.DataFrame`
The dataset to make predictions for. Should contain same column names as training Dataset and follow same format
(may contain extra columns that won't be used by Predictor, including the label-column itself).
If str is passed, `dataset` will be loaded using the str value as the file path.
model : str (optional)
The name of the model to get predictions from. Defaults to None, which uses the highest scoring model on the validation set.
Valid models are listed in this `predictor` by calling `predictor.get_model_names()`
as_pandas : bool (optional)
Whether to return the output as a pandas Series (True) or numpy array (False)

Returns

Array of predictions, one corresponding to each row in given dataset. Either numpy Ndarray or pandas Series depending on `as_pandas` argument.
Use trained models to produce predicted labels ( in classification ) or response values ( in regression ) . Parameters dataset : str or : class : ` TabularDataset ` or ` pandas . DataFrame ` The dataset to make predictions for . Should contain same column names as training Dataset and follow same format ( may contain extra columns that won ' t be used by Predictor including the label - column itself ) . If str is passed ` dataset ` will be loaded using the str value as the file path . model : str ( optional ) The name of the model to get predictions from . Defaults to None which uses the highest scoring model on the validation set . Valid models are listed in this ` predictor ` by calling ` predictor . get_model_names () ` as_pandas : bool ( optional ) Whether to return the output as a pandas Series ( True ) or numpy array ( False ) Returns Array of predictions one corresponding to each row in given dataset . Either numpy Ndarray or pandas Series depending on ` as_pandas ` argument .
def predict(self, dataset, model=None, as_pandas=False):
        dataset = self.__get_dataset(dataset)
        return self._learner.predict(X=dataset, model=model, as_pandas=as_pandas)
def predict ( self , dataset , model = None , as_pandas = False ) : dataset = self . __get_dataset ( dataset ) return self . _learner . predict ( X = dataset , model = model , as_pandas = as_pandas )
Use trained models to produce predicted labels (in classification) or response values (in regression).
Use trained models to produce predicted labels ( in classification ) or response values ( in regression ) .
""" Use trained models to produce predicted labels (in classification) or response values (in regression).

            Parameters
            ----------
            dataset : str or :class:`TabularDataset` or `pandas.DataFrame`
                The dataset to make predictions for. Should contain same column names as training Dataset and follow same format
                (may contain extra columns that won't be used by Predictor, including the label-column itself).
                If str is passed, `dataset` will be loaded using the str value as the file path.
            model : str (optional)
                The name of the model to get predictions from. Defaults to None, which uses the highest scoring model on the validation set.
                Valid models are listed in this `predictor` by calling `predictor.get_model_names()`
            as_pandas : bool (optional)
                Whether to return the output as a pandas Series (True) or numpy array (False)

            Returns
            -------
            Array of predictions, one corresponding to each row in given dataset. Either numpy Ndarray or pandas Series depending on `as_pandas` argument.

        """
self
dataset
model
as_pandas