def roc_auc(model, X, y=None, ax=None, **kwargs):
    """ROCAUC Quick method:

    Receiver Operating Characteristic (ROC) curves are a measure of a
    classifier's predictive quality that compares and visualizes the tradeoff
    between the models' sensitivity and specificity. The ROC curve displays
    the true positive rate on the Y axis and the false positive rate on the
    X axis on both a global average and per-class basis. The ideal point is
    therefore the top-left corner of the plot: false positives are zero and
    true positives are one.

    This leads to another metric, area under the curve  (AUC), a computation
    of the relationship between false positives and true positives. The higher
    the AUC, the better the model generally is. However, it is also important
    to inspect the "steepness" of the curve, as this describes the
    maximization of the true positive rate while minimizing the false positive
    rate. Generalizing "steepness" usually leads to discussions about
    convexity, which we do not get into here.

    Parameters
    ----------
    model : the Scikit-Learn estimator
        Should be an instance of a classifier, else the __init__ will
        return an error.

    X : ndarray or DataFrame of shape n x m
        A matrix of n instances with m features

    y : ndarray or Series of length n
        An array or series of target or class values

    ax : the axis to plot the figure on.

    classes : list
        A list of class names for the legend. If classes is None and a y value
        is passed to fit then the classes are selected from the target vector.
        Note that the curves must be computed based on what is in the target
        vector passed to the ``score()`` method. Class names are used for
        labeling only and must be in the correct order to prevent confusion.

    micro : bool, default = True
        Plot the micro-averages ROC curve, computed from the sum of all true
        positives and false positives across all classes. Micro is not defined
        for binary classification problems with estimators with only a
        decision_function method.

    macro : bool, default = True
        Plot the macro-averages ROC curve, which simply takes the average of
        curves across all classes. Macro is not defined for binary
        classification problems with estimators with only a decision_function
        method.

    per_class : bool, default = True
        Plot the ROC curves for each individual class. This should be set
        to false if only the macro or micro average curves are required. Per-
        class classification is not defined for binary classification problems
        with estimators with only a decision_function method.

    Notes
    -----
    ROC curves are typically used in binary classification, and in fact the
    Scikit-Learn ``roc_curve`` metric is only able to perform metrics for
    binary classifiers. As a result it is necessary to binarize the output or
    to use one-vs-rest or one-vs-all strategies of classification. The
    visualizer does its best to handle multiple situations, but exceptions can
    arise from unexpected models or outputs.

    Another important point is the relationship of class labels specified on
    initialization to those drawn on the curves. The classes are not used to
    constrain ordering or filter curves; the ROC computation happens on the
    unique values specified in the target vector to the ``score`` method. To
    ensure the best quality visualization, do not use a LabelEncoder for this
    and do not pass in class labels.

    .. seealso:: http://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html
    .. todo:: Allow the class list to filter the curves on the visualization.

    Examples
    --------
    >>> from yellowbrick.classifier import ROCAUC
    >>> from sklearn.linear_model import LogisticRegression
    >>> data = load_data("occupancy")
    >>> features = ["temp", "relative humidity", "light", "C02", "humidity"]
    >>> X = data[features].values
    >>> y = data.occupancy.values
    >>> roc_auc(LogisticRegression(), X, y)

    Returns
    -------
    ax : matplotlib axes
        Returns the axes that the roc-auc curve was drawn on.
    """
    # Instantiate the visualizer
    visualizer = ROCAUC(model, ax, **kwargs)

    # Create the train and test splits
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

    # Fit and transform the visualizer (calls draw)
    visualizer.fit(X_train, y_train, **kwargs)
    visualizer.score(X_test, y_test)
    visualizer.finalize()

    # Return the axes object on the visualizer
    return visualizer.ax
ROCAUC Quick method:

    Receiver Operating Characteristic (ROC) curves are a measure of a
    classifier's predictive quality that compares and visualizes the tradeoff
    between the models' sensitivity and specificity. The ROC curve displays
    the true positive rate on the Y axis and the false positive rate on the
    X axis on both a global average and per-class basis. The ideal point is
    therefore the top-left corner of the plot: false positives are zero and
    true positives are one.

    This leads to another metric, area under the curve  (AUC), a computation
    of the relationship between false positives and true positives. The higher
    the AUC, the better the model generally is. However, it is also important
    to inspect the "steepness" of the curve, as this describes the
    maximization of the true positive rate while minimizing the false positive
    rate. Generalizing "steepness" usually leads to discussions about
    convexity, which we do not get into here.

    Parameters
    ----------
    model : the Scikit-Learn estimator
        Should be an instance of a classifier, else the __init__ will
        return an error.

    X : ndarray or DataFrame of shape n x m
        A matrix of n instances with m features

    y : ndarray or Series of length n
        An array or series of target or class values

    ax : the axis to plot the figure on.

    classes : list
        A list of class names for the legend. If classes is None and a y value
        is passed to fit then the classes are selected from the target vector.
        Note that the curves must be computed based on what is in the target
        vector passed to the ``score()`` method. Class names are used for
        labeling only and must be in the correct order to prevent confusion.

    micro : bool, default = True
        Plot the micro-averages ROC curve, computed from the sum of all true
        positives and false positives across all classes. Micro is not defined
        for binary classification problems with estimators with only a
        decision_function method.

    macro : bool, default = True
        Plot the macro-averages ROC curve, which simply takes the average of
        curves across all classes. Macro is not defined for binary
        classification problems with estimators with only a decision_function
        method.

    per_class : bool, default = True
        Plot the ROC curves for each individual class. This should be set
        to false if only the macro or micro average curves are required. Per-
        class classification is not defined for binary classification problems
        with estimators with only a decision_function method.

    Notes
    -----
    ROC curves are typically used in binary classification, and in fact the
    Scikit-Learn ``roc_curve`` metric is only able to perform metrics for
    binary classifiers. As a result it is necessary to binarize the output or
    to use one-vs-rest or one-vs-all strategies of classification. The
    visualizer does its best to handle multiple situations, but exceptions can
    arise from unexpected models or outputs.

    Another important point is the relationship of class labels specified on
    initialization to those drawn on the curves. The classes are not used to
    constrain ordering or filter curves; the ROC computation happens on the
    unique values specified in the target vector to the ``score`` method. To
    ensure the best quality visualization, do not use a LabelEncoder for this
    and do not pass in class labels.

    .. seealso:: http://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html
    .. todo:: Allow the class list to filter the curves on the visualization.

    Examples
    --------
    >>> from yellowbrick.classifier import ROCAUC
    >>> from sklearn.linear_model import LogisticRegression
    >>> data = load_data("occupancy")
    >>> features = ["temp", "relative humidity", "light", "C02", "humidity"]
    >>> X = data[features].values
    >>> y = data.occupancy.values
    >>> roc_auc(LogisticRegression(), X, y)

    Returns
    -------
    ax : matplotlib axes
        Returns the axes that the roc-auc curve was drawn on.