def METHOD_NAME(
    y_true,
    y_probas,
    title="Lift Curve",
    ax=None,
    figsize=None,
    title_fontsize="large",
    text_fontsize="medium",
    pos_label=None,
):
    """
    This method is copied from scikit-plot package.
    See https://github.com/reiinakano/scikit-plot/blob/2dd3e6a76df77edcbd724c4db25575f70abb57cb/scikitplot/metrics.py#L1133
    Generates the Lift Curve from labels and scores/probabilities
    The lift curve is used to determine the effectiveness of a
    binary classifier. A detailed explanation can be found at
    http://www2.cs.uregina.ca/~dbd/cs831/notes/lift_chart/lift_chart.html.
    The implementation here works only for binary classification.
    Args:
        y_true (array-like, shape (n_samples)):
            Ground truth (correct) target values.
        y_probas (array-like, shape (n_samples, n_classes)):
            Prediction probabilities for each class returned by a classifier.
        title (string, optional): Title of the generated plot. Defaults to
            "Lift Curve".
        ax (:class:`matplotlib.axes.Axes`, optional): The axes upon which to
            plot the learning curve. If None, the plot is drawn on a new set of
            axes.
        figsize (2-tuple, optional): Tuple denoting figure size of the plot
            e.g. (6, 6). Defaults to ``None``.
        title_fontsize (string or int, optional): Matplotlib-style fontsizes.
            Use e.g. "small", "medium", "large" or integer-values. Defaults to
            "large".
        text_fontsize (string or int, optional): Matplotlib-style fontsizes.
            Use e.g. "small", "medium", "large" or integer-values. Defaults to
            "medium".
        pos_label (optional): Label for the positive class.
    Returns:
        ax (:class:`matplotlib.axes.Axes`): The axes on which the plot was
            drawn.
    Example:
        >>> lr = LogisticRegression()
        >>> lr = lr.fit(X_train, y_train)
        >>> y_probas = lr.predict_proba(X_test)
        >>> plot_lift_curve(y_test, y_probas)
        <matplotlib.axes._subplots.AxesSubplot object at 0x7fe967d64490>
        >>> plt.show()
        .. image:: _static/examples/plot_lift_curve.png
           :align: center
           :alt: Lift Curve
    """
    y_true = np.array(y_true)
    y_probas = np.array(y_probas)
    classes = np.unique(y_true)
    if len(classes) != 2:
        raise ValueError(f"Cannot calculate Lift Curve for data with {len(classes)} category/ies")
    # Compute Cumulative Gain Curves
    percentages, gains1 = _cumulative_gain_curve(y_true, y_probas[:, 0], classes[0])
    percentages, gains2 = _cumulative_gain_curve(y_true, y_probas[:, 1], classes[1])
    percentages = percentages[1:]
    gains1 = gains1[1:]
    gains2 = gains2[1:]
    gains1 = gains1 / percentages
    gains2 = gains2 / percentages
    if ax is None:
        _, ax = plt.subplots(1, 1, figsize=figsize)
    ax.set_title(title, fontsize=title_fontsize)
    label0 = f"Class {classes[0]}"
    label1 = f"Class {classes[1]}"
    # show (positive) next to the positive class in the legend
    if pos_label:
        if pos_label == classes[0]:
            label0 = f"Class {classes[0]} (positive)"
        elif pos_label == classes[1]:
            label1 = f"Class {classes[1]} (positive)"
        # do not mark positive class if pos_label is not in classes
    ax.plot(percentages, gains1, lw=3, label=label0)
    ax.plot(percentages, gains2, lw=3, label=label1)
    ax.plot([0, 1], [1, 1], "k--", lw=2, label="Baseline")
    ax.set_xlabel("Percentage of sample", fontsize=text_fontsize)
    ax.set_ylabel("Lift", fontsize=text_fontsize)
    ax.tick_params(labelsize=text_fontsize)
    ax.grid("on")
    ax.legend(loc="best", fontsize=text_fontsize)
    return ax