docstr: Set up the unit test by loading the dataset and training a model.
docstr_tok: ["Set","up","the","unit","test","by","loading","the","dataset","and","training","a","model","."]
code: def _train_convert_evaluate_assert(
      self, bt_params={}, allowed_error={}, **params
  ):
      """
      Set up the unit test by loading the dataset and training a model.
      """
      # Train a model
      xgb_model = xgboost.train(bt_params, self.dtrain, **params)
  
      # Convert the model
      spec = xgb_converter.convert(
          xgb_model,
          self.feature_names,
          self.output_name,
          force_32bit_float=False,
      )
  
      if _is_macos() and _macos_version() >= (10, 13):
          # Get predictions
          df = pd.DataFrame(self.X, columns=self.feature_names)
          df["target"] = xgb_model.predict(self.dtrain)
  
          # Evaluate it
          metrics = evaluate_regressor(
              spec, df, target="target", verbose=False
          )
          self._check_metrics(metrics, allowed_error, bt_params)
  
imports: import itertools
  import unittest
  import pandas
  import pytest
  from coremltools._deps import _HAS_SKLEARN, _HAS_XGBOOST
  from coremltools.models.utils import _is_macos, _macos_version, evaluate_regressor
label_desc: Use image node_id 3 for calling the XgboostBoosterBostonHousingNumericTest obj's underlying member method code with example usage: obj._train_convert_evaluate_assert(bt_params, allowed_error) without return types