class_docstr: Unit test class for testing scikit-learn converter and running both models
docstr: Train a scikit-learn model, convert it and then evaluate it with CoreML
docstr_tok: ["Train","a","scikit-learn","model",",","convert","it","and","then","evaluate","it","with","CoreML"]
code: def _train_convert_evaluate_assert(self, **scikit_params):
      """
      Train a scikit-learn model, convert it and then evaluate it with CoreML
      """
      scikit_model = RandomForestRegressor(
          random_state=1, **scikit_params
      )
      scikit_model.fit(self.X, self.target)
  
      # Convert the model
      spec = skl_converter.convert(
          scikit_model, self.feature_names, self.output_name
      )
  
      if _is_macos() and _macos_version() >= (10, 13):
          # Get predictions
          df = pd.DataFrame(self.X, columns=self.feature_names)
          df["target"] = scikit_model.predict(self.X)
  
          # Evaluate it
          metrics = evaluate_regressor(spec, df, verbose=False)
          self._check_metrics(metrics, scikit_params)
  
file_path: coremltools/coremltools/test/sklearn_tests/test_random_forest_regression_numeric.py
imports: import itertools
  import unittest
  import pandas
  import pytest
  from coremltools._deps import _HAS_SKLEARN
  from coremltools.models.utils import _is_macos, _macos_version, evaluate_regressor
label_desc: Use image node_id 3 for calling the RandomForestRegressorBostonHousingScikitNumericTest obj's underlying member method code with example usage: obj._train_convert_evaluate_assert() without return types