class_docstr: Evaluate metrics for y_true and y_pred.
docstr: Evaluate a specific metric for y_true and y_pred.
  
  :param metric: String in ['me', 'mae', 'mse', 'rmse', 'msle', 'r2'
         , 'mpe', 'mape', 'mspe', 'smape', 'mdape', 'smdape', 'accuracy']
  :param y_true: Array-like of shape = (n_samples, \*).
         Ground truth (correct) target values.
  :param y_pred: Array-like of shape = (n_samples, \*).
         Estimated target values.
  :param multioutput: String in ['raw_values', 'uniform_average']
  
  :return: Float or ndarray of floats.
           A floating point value, or an
           array of floating point values, one for each individual target.
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code: def evaluate(metric, y_true, y_pred, multioutput="raw_values"):
      """
      Evaluate a specific metric for y_true and y_pred.
  
      :param metric: String in ['me', 'mae', 'mse', 'rmse', 'msle', 'r2'
             , 'mpe', 'mape', 'mspe', 'smape', 'mdape', 'smdape', 'accuracy']
      :param y_true: Array-like of shape = (n_samples, \*).
             Ground truth (correct) target values.
      :param y_pred: Array-like of shape = (n_samples, \*).
             Estimated target values.
      :param multioutput: String in ['raw_values', 'uniform_average']
  
      :return: Float or ndarray of floats.
               A floating point value, or an
               array of floating point values, one for each individual target.
      """
      Evaluator.check_metric(metric)
      result = Evaluator.metrics_func[metric](
          y_true, y_pred, multioutput=multioutput
      )
      if result.shape == (1,):
          return result[0]
      else:
          return result
  
imports: from sklearn.metrics import mean_squared_error
  from sklearn.metrics import r2_score
  from sklearn.metrics import mean_absolute_error
  from sklearn.metrics import mean_squared_log_error
  import numpy
  import pandas
label_desc: Use image node_id 1 for calling the Evaluator obj's underlying member method code with example usage: obj.evaluate(metric, y_true, y_pred, multioutput) and returns: result, result