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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def create(dataset, target, features=None, validation_set = 'auto', verbose=True):
 """ Automatically create a suitable regression model based on the provided training data. To use specific options of a desired model, use the ``create`` function of the corresponding model. Parameters dataset : SFrame Dataset for training the model. target : str The name of the column in ``dataset`` that is the prediction target. This column must have a numeric type (int/float). features : list[string], optional Names of the columns containing features. 'None' (the default) indicates that all columns except the target variable should be used as features. The features are columns in the input SFrame that can be of the following types: - *Numeric*: values of numeric type integer or float. - *Categorical*: values of type string. - *Array*: list of numeric (integer or float) values. Each list element is treated as a separate feature in the model. - *Dictionary*: key-value pairs with numeric (integer or float) values Each key of a dictionary is treated as a separate feature and the value in the dictionary corresponds to the value of the feature. Dictionaries are ideal for representing sparse data. Columns of type *list* are not supported. Convert such feature columns to type array if all entries in the list are of numeric types. If the lists contain data of mixed types, separate them out into different columns. validation_set : SFrame, optional A dataset for monitoring the model's generalization performance. For each row of the progress table, the chosen metrics are computed for both the provided training dataset and the validation_set. The format of this SFrame must be the same as the training set. By default this argument is set to 'auto' and a validation set is automatically sampled and used for progress printing. If validation_set is set to None, then no additional metrics are computed. The default value is 'auto'. verbose : boolean, optional If True, print progress information during training. Returns ------- out : A trained regression model. See Also -------- turicreate.linear_regression.LinearRegression, turicreate.boosted_trees_regression.BoostedTreesRegression Examples -------- .. sourcecode:: python # Setup the data # Selects the best model based on your data. # Make predictions and evaluate results. # Setup the data # Selects the best model based on your data. # Make predictions and evaluate results. """


    dataset, validation_set = _validate_data(dataset, target, features,
                                             validation_set)
    if validation_set is None:
        validation_set = _turicreate.SFrame()

    model_proxy = _turicreate.extensions.create_automatic_regression_model(
        dataset, target, validation_set, {})

    return _sl.wrap_model_proxy(model_proxy)