new_contents: import sys
  sys.path.insert(1,"../../")
  import h2o
  from tests import pyunit_utils
  from h2o.estimators.gbm import H2OGradientBoostingEstimator
  
  
  def pyunit_mean_per_class_error():
      gbm = H2OGradientBoostingEstimator(nfolds=3, fold_assignment="Random", seed=1234)
  
      ## Binomial
      cars = h2o.import_file("/users/arno/h2o-3/smalldata/junit/cars_20mpg.csv")
      cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
      r = cars[0].runif(seed=1234)
      train = cars[r > .2]
      valid = cars[r <= .2]
      response_col = "economy_20mpg"
      predictors = ["displacement","power","weight","acceleration","year"]
      gbm.distribution = "bernoulli"
      gbm.train(y=response_col, x=predictors, validation_frame=valid, training_frame=train)
      print(gbm)
      mpce = gbm.mean_per_class_error([0.5,0.8]) ## different thresholds
      assert (abs(mpce[0][1] - 0.004132231404958664) < 1e-5)
      assert (abs(mpce[1][1] - 0.021390374331550777) < 1e-5)
  
  
  
      ## Multinomial
      cars = h2o.import_file("/users/arno/h2o-3/smalldata/junit/cars_20mpg.csv")
      cars["cylinders"] = cars["cylinders"].asfactor()
      r = cars[0].runif(seed=1234)
      train = cars[r > .2]
      valid = cars[r <= .2]
      response_col = "cylinders"
      predictors = ["displacement","power","weight","acceleration","year"]
      gbm.distribution="multinomial"
      gbm.train(x=predictors,y=response_col, training_frame=train, validation_frame=valid)
      print(gbm)
      mpce = gbm.mean_per_class_error(train=True)
      assert( mpce == 0 )
      mpce = gbm.mean_per_class_error(valid=True)
      assert(abs(mpce - 0.207142857143 ) < 1e-5)
      mpce = gbm.mean_per_class_error(xval=True)
      assert(abs(mpce - 0.350071715433 ) < 1e-5)
  
  if __name__ == "__main__":
      pyunit_utils.standalone_test(pyunit_mean_per_class_error)
  else:
      pyunit_mean_per_class_error
  
subject: Add extra Python test for mean-per-class error.