def _roc(y_true, y_score, ax=None):
    """
    Plot ROC curve for binary classification.

    Parameters
    ----------
    y_true : array-like, shape = [n_samples]
        Correct target values (ground truth).
    y_score : array-like, shape = [n_samples]
        Target scores (estimator predictions).
    ax: matplotlib Axes
        Axes object to draw the plot onto, otherwise uses current Axes

    Returns
    -------
    ax: matplotlib Axes
        Axes containing the plot

    """
    # check dimensions

    fpr, tpr, _ = roc_curve(y_true, y_score)
    roc_auc = auc(fpr, tpr)

    ax.plot(fpr, tpr, label=('ROC curve (area = {0:0.2f})'.format(roc_auc)))
    _set_ax_settings(ax)
    return ax
def _roc ( y_true , y_score , ax = None ) : fpr , tpr , _ = roc_curve ( y_true , y_score ) roc_auc = auc ( fpr , tpr ) ax . plot ( fpr , tpr , label = ( 'ROC curve (area = {0:0.2f})' . format ( roc_auc ) ) ) _set_ax_settings ( ax ) return ax
Plot ROC curve for binary classification.

    Parameters
    ----------
    y_true : array-like, shape = [n_samples]
        Correct target values (ground truth).
    y_score : array-like, shape = [n_samples]
        Target scores (estimator predictions).
    ax: matplotlib Axes
        Axes object to draw the plot onto, otherwise uses current Axes

    Returns
    -------
    ax: matplotlib Axes
        Axes containing the plot