import h2o

h2o.init(nthreads = -1, max_mem_size = "110g")
df = h2o.import_file(path = "C:/user/path_to_data.csv")

#predictors
x = [col for col in df.column if 'target' not in col]
y = 'target'

#establish model
m = h2o.estimators.H2ORandomForestEstimator(model_id="RF_defaults", nfolds = 5)

#train
m.train(x,y, df)

#view results
print (m.cross_validation_metrics_summary())