code: def min_split_improvement():
      train = h2o.upload_file(
          pyunit_utils.locate("smalldata/covtype/covtype.20k.data")
      )
      train["C55"] = train["C55"].asfactor()
  
      hh1 = H2ORandomForestEstimator(ntrees=20, min_split_improvement=0)
      hh2 = H2ORandomForestEstimator(
          ntrees=20, min_split_improvement=1e-1
      )
  
      hh1.train(x=list(range(55)), y="C55", training_frame=train)
      hh2.train(x=list(range(55)), y="C55", training_frame=train)
  
      assert (
          hh1.logloss() < hh2.logloss()
      ), "Expected logloss to get worse with large min_split_improvement"
  
imports: from builtins import range
  import sys
  import h2o
  from tests import pyunit_utils
  from h2o.estimators.random_forest import H2ORandomForestEstimator
label_desc: Use image node_id 1 for calling a global function with example usage: min_split_improvement() without return types