class_docstr: CaseEvaluationResults stores aggregated statistics for one EvaluationCase and one metric.
class_docstr_tok: ["CaseEvaluationResults","stores","aggregated","statistics","for","one","EvaluationCase","and","one","metric","."]
docstr: :param offset: First iteration to plot
  :return: plotly Figure with learning curves for each fold
docstr_tok: [":","param","offset",":","First","iteration","to","plot",":","return",":","plotly","Figure","with","learning","curves","for","each","fold"]
code: def create_learning_curves_plot(self, offset=None):
      """
  
      :param offset: First iteration to plot
      :return: plotly Figure with learning curves for each fold
      """
      import plotly.graph_objs as go
  
      traces = []
  
      for fold in self.get_fold_ids():
          scores_curve = self.get_fold_curve(fold)
          if offset is not None:
              first_idx = offset
          else:
              first_idx = int(len(scores_curve) * 0.1)
  
          traces.append(
              go.Scatter(
                  x=[
                      i * int(self._eval_step)
                      for i in range(first_idx, len(scores_curve))
                  ],
                  y=scores_curve[first_idx:],
                  mode="lines",
                  name="Fold #{}".format(fold),
              )
          )
  
      layout = go.Layout(
          title="Learning curves for case {}".format(self._case),
          hovermode="closest",
          xaxis=dict(
              title="Iteration",
              ticklen=5,
              zeroline=False,
              gridwidth=2,
          ),
          yaxis=dict(
              title="Metric",
              ticklen=5,
              gridwidth=2,
          ),
          showlegend=True,
      )
      fig = go.Figure(data=traces, layout=layout)
      return fig
  
imports: import numpy
  import pandas
  from enum import Enum
  from ..None import CatBoostError
  from ..core import metric_description_or_str_to_str
  from ..utils import compute_wx_test
label_desc: Use image node_id 14 for calling the CaseEvaluationResult obj's underlying member method code with example usage: obj.create_learning_curves_plot(offset) and returns: fig