code: def plot_lift_curve(
      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
  
imports: import matplotlib.pyplot
  import numpy
label_desc: Use image node_id 2 for calling a global function with example usage: plot_lift_curve(y_true, y_probas, title, ax, figsize, title_fontsize, text_fontsize, pos_label) and returns: ax