code: def _check_params(self):
          
          if self.n_estimators <= 0:
              raise ValueError("n_estimators must be greater than 0 but "
                               "was %r" % self.n_estimators)
  
          if not 0.0 < self.subsample <= 1.0:
              raise ValueError("subsample must be in ]0; 1] but "
                               "was %r" % self.subsample)
  
          if not 0.0 < self.learning_rate <= 1.0:
              raise ValueError("learning_rate must be within ]0; 1] but "
                               "was %r" % self.learning_rate)
  
          if not 0.0 <= self.dropout_rate < 1.0:
              raise ValueError("dropout_rate must be within [0; 1[, but "
                               "was %r" % self.dropout_rate)
  
          if self.loss not in LOSS_FUNCTIONS:
              raise ValueError("Loss '{0:s}' not supported. ".format(self.loss))
docs: Check validity of parameters and raise ValueError if not valid.