class_docstr: Igel is the base model to use the fit, evaluate and predict functions of the sklearn library
class_docstr_tok: ["Igel","is","the","base","model","to","use","the","fit",",","evaluate","and","predict","functions","of","the","sklearn","library"]
docstr: save the model to a binary file
  @param model: model to save
  @return: bool
docstr_tok: ["save","the","model","to","a","binary","file","@","param","model",":","model","to","save","@","return",":","bool"]
code: def _save_model(self, model):
      """
      save the model to a binary file
      @param model: model to save
      @return: bool
      """
      try:
          if not os.path.exists(self.results_path):
              logger.info(
                  f"creating model_results folder to save results...\n"
                  f"path of the results folder: {self.results_path}"
              )
              os.mkdir(self.results_path)
          else:
              logger.info(f"Folder {self.results_path} already exists")
              logger.warning(
                  f"data in the {self.results_path} folder will be overridden. If you don't "
                  f"want this, then move the current {self.results_path} to another path"
              )
  
      except OSError:
          logger.exception(
              f"Creating the directory {self.results_path} failed "
          )
      else:
          logger.info(
              f"Successfully created the directory in {self.results_path} "
          )
          joblib.dump(model, open(self.default_model_path, "wb"))
          return True
  
imports: import json
  import logging
  import os
  import warnings
  import joblib
  import numpy
  import pandas
  from sklearn.model_selection import cross_validate, train_test_split
  from sklearn.multioutput import MultiOutputClassifier, MultiOutputRegressor
  from skl2onnx import convert_sklearn
  from skl2onnx.common.data_types import FloatTensorType
label_desc: Use image node_id 3 for calling the Igel obj's underlying member method code with example usage: obj._save_model(model) and returns: True