class_docstr: evaluation result class for binary classification problem
class_docstr_tok: ["evaluation","result","class","for","binary","classification","problem"]
docstr: converts the metrics saved in the object to a table that is ready to render in the report.
  
  Returns:
      a set of tables (title, header, values)
docstr_tok: ["converts","the","metrics","saved","in","the","object","to","a","table","that","is","ready","to","render","in","the","report",".","Returns",":","a","set","of","tables","(","title",",","header",",","values",")"]
code: def convert_metrics_to_table(self):
      """
      converts the metrics saved in the object to a table that is ready to render in the report.
  
      Returns:
          a set of tables (title, header, values)
      """
  
      table_header = ["Metric", "Value"]
      table_content = []
      for metric in self.metric_set:
          table_content.append(
              [metric.capitalize(), "%.4f" % self.resultdict[metric][1]]
          )
      layout = get_table_layout(table_header)
      output_tables = (
          "Overall Result",
          table_header,
          table_content,
          layout,
      )
      return output_tables
  
file_path: contextual-ai/xai/model/evaluation/binary_classification_result.py
imports: from typing import List
  from xai import constants
  from xai.model.evaluation.basic_result import ClassificationResult
  from xai.model.evaluation.confusion_matrix import ConfusionMatrix
  from xai.util import get_table_layout
label_desc: Use image node_id 3 for calling the BinaryClassificationResult obj's underlying member method code with example usage: obj.convert_metrics_to_table() and returns: output_tables