code: def evaluate_classifier(model, data, target="target", verbose=False):
      """
      Evaluate a Core ML classifier model and compare against predictions
      from the original framework (for testing correctness of conversion).
      Use this evaluation for models that don't deal with probabilities.
  
      Parameters
      ----------
      filename: list of str or list of MLModel
          File from where to load the model from (OR) a loaded
          version of the MLModel.
  
      data: list of str or list of Dataframe
          Test data on which to evaluate the models (dataframe,
          or path to a csv file).
  
      target: str
         Column to interpret as the target column
  
      verbose: bool
         Set to true for a more verbose output.
  
      See Also
      --------
      evaluate_regressor, evaluate_classifier_with_probabilities
  
      Examples
      --------
      .. sourcecode:: python
  
          >>> metrics =  coremltools.utils.evaluate_classifier(spec, 'data_and_predictions.csv', 'target')
          >>> print(metrics)
          {"samples": 10, num_errors: 0}
      """
      model = _get_model(model)
      if verbose:
          print("")
          print("Other Framework\t\tPredicted")
  
      num_errors = 0
  
      for index, row in data.iterrows():
          predicted = model.predict(dict(row))[_to_unicode(target)]
          other_framework = row["prediction"]
          if predicted != other_framework:
              num_errors += 1
  
          if verbose:
              print("{}\t\t\t\t{}".format(other_framework, predicted))
  
      ret = {"num_samples": len(data), "num_errors": num_errors}
  
      if verbose:
          print("results: {}".format(ret))
  
      return ret
  
imports: import math
  import numpy
  import os
  import six
  import warnings
  import sys
  from coremltools.proto import Model_pb2
  from coremltools.models._deprecation import deprecated
  from .._deps import _HAS_SKLEARN
label_desc: Use image node_id 14 for calling a global function with example usage: evaluate_classifier(model, data, target, verbose) and returns: ret