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"]
code: def get_evaluation(self, model, x_test, y_true, y_pred, **kwargs):
      try:
          res = evaluate_model(
              model_type=self.model_type,
              model=model,
              x_test=x_test,
              y_pred=y_pred,
              y_true=y_true,
              get_score_only=False,
              **kwargs,
          )
      except Exception as e:
          logger.debug(e)
          res = evaluate_model(
              model_type=self.model_type,
              model=model,
              x_test=x_test,
              y_pred=y_pred,
              y_true=y_true,
              get_score_only=True,
              **kwargs,
          )
      return res
  
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 10 for calling the Igel obj's underlying member method code with example usage: obj.get_evaluation(model, x_test, y_true, y_pred) and returns: res