class_docstr: Wrapper class to handle ensemble algorithms that use multiple forecasters.
  
  This implements default methods for setting uniform weights, updating
  and prediction.
  
  Parameters
  ----------
  n_estimators : float
      number of estimators
  loss_func : function
      loss function which follows sklearn.metrics API, for updating weights
class_docstr_tok: ["Wrapper","class","to","handle","ensemble","algorithms","that","use","multiple","forecasters",".","This","implements","default","methods","for","setting","uniform","weights",",","updating","and","prediction",".","Parameters","--","--","--","--","--","n_estimators",":","float","number","of","estimators","loss_func",":","function","loss","function","which","follows","sklearn.metrics","API",",","for","updating","weights"]
code: def __init__(self, n_estimators=10, loss_func=None):
      self.n_estimators = n_estimators
      self.weights = np.ones(n_estimators) / n_estimators
      self.loss_func = loss_func
      super().__init__()
  
imports: import numpy
  from scipy.optimize import bisect, nnls
label_desc: Use image node_id 1 to create a new _PredictionWeightedEnsembler object  with example: obj = _PredictionWeightedEnsembler(n_estimators, loss_func)