class_docstr: Base class for defining metrics in sktime.
  
  Extends sktime BaseObject.
class_docstr_tok: ["Base","class","for","defining","metrics","in","sktime",".","Extends","sktime","BaseObject","."]
docstr: Calculate metric value using underlying metric function.
  
  Parameters
  ----------
  y_true : ground truth prediction target values
      type depending on the loss type, abstract descendants
  
  y_pred : predicted values
      type depending on the loss type, abstract descendants
  
  Returns
  -------
  loss : float
      Calculated loss metric.
docstr_tok: ["Calculate","metric","value","using","underlying","metric","function",".","Parameters","--","--","--","--","--","y_true",":","ground","truth","prediction","target","values","type","depending","on","the","loss","type",",","abstract","descendants","y_pred",":","predicted","values","type","depending","on","the","loss","type",",","abstract","descendants","Returns","--","--","--","-","loss",":","float","Calculated","loss","metric","."]
code: def evaluate(self, y_true, y_pred, **kwargs):
      """Calculate metric value using underlying metric function.
  
      Parameters
      ----------
      y_true : ground truth prediction target values
          type depending on the loss type, abstract descendants
  
      y_pred : predicted values
          type depending on the loss type, abstract descendants
  
      Returns
      -------
      loss : float
          Calculated loss metric.
      """
      raise NotImplementedError("abstract method")
  
imports: from sktime.base import BaseObject
label_desc: Use image node_id 3 for calling the BaseMetric obj's underlying member method code with example usage: obj.evaluate(y_true, y_pred) without return types