Create a :class:`~turicreate.toolkits.SupervisedLearningModel`,

    This is generic function that allows you to create any model that
    implements SupervisedLearningModel This function is normally not called, call
    specific model's create function instead

    Parameters
    ----------
    dataset : SFrame
        Dataset for training the model.

    target : string
        Name of the column containing the target variable. The values in this
        column must be 0 or 1, of integer type.

    model_name : string
        Name of the model

    features : list[string], optional
        List of feature names used by feature column

    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'.

    distributed: env
        The distributed environment

    verbose : boolean
        whether print out messages during training

    seed : int, optional
        Seed for random number generation. Set this value to ensure that the
        same model is created every time.

    kwargs : dict
        Additional parameter options that can be passed

def create(dataset, target, model_name, features=None,
           validation_set='auto', distributed='auto',
           verbose=True, seed=None, **kwargs):
    """
    Create a :class:`~turicreate.toolkits.SupervisedLearningModel`,

    This is generic function that allows you to create any model that
    implements SupervisedLearningModel This function is normally not called, call
    specific model's create function instead

    Parameters
    ----------
    dataset : SFrame
        Dataset for training the model.

    target : string
        Name of the column containing the target variable. The values in this
        column must be 0 or 1, of integer type.

    model_name : string
        Name of the model

    features : list[string], optional
        List of feature names used by feature column

    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'.

    distributed: env
        The distributed environment

    verbose : boolean
        whether print out messages during training

    seed : int, optional
        Seed for random number generation. Set this value to ensure that the
        same model is created every time.

    kwargs : dict
        Additional parameter options that can be passed
    """

    # Perform error-checking and trim inputs to specified columns
    dataset, validation_set = _validate_data(dataset, target, features,
                                             validation_set)

    # Sample a validation set from the training data if requested
    if isinstance(validation_set, str):
        assert validation_set == 'auto'
        if dataset.num_rows() >= 100:
            if verbose:
                print_validation_track_notification()
            dataset, validation_set = dataset.random_split(.95, seed=seed, exact=True)
        else:
            validation_set = _turicreate.SFrame()
    elif validation_set is None:
        validation_set = _turicreate.SFrame()

    # Sanitize model-specific options
    options = {k.lower(): kwargs[k] for k in kwargs}

    # Create a model instance and train it
    model = _turicreate.extensions.__dict__[model_name]()
    with QuietProgress(verbose):
        model.train(dataset, target, validation_set, options)

    return SupervisedLearningModel(model, model_name)