code: def test_compare_R():
      myX = [
          "c_0",
          "c_1",
          "c_2",
          "c_3",
          "c_4",
          "c_5",
          "c_6",
          "c_7",
          "c_8",
          "c_9",
          "C1",
          "C2",
          "C3",
          "C4",
          "C5",
          "C6",
          "C7",
          "C8",
          "C9",
          "C10",
      ]
      myY = "response"
      gamCols = [["c_0"], ["c_1", "c_2"], ["c_3", "c_4", "c_5"]]
      bsT = [1, 1, 1]
      scaleP = [0.001, 0.001, 0.001]
      numKnots = [10, 10, 12]
      print("Comparing H2O and R GAM performance for binomial")
      dataBinomial = h2o.import_file(
          pyunit_utils.locate(
              "smalldata/gam_test/synthetic_20Cols_binomial_20KRows.csv"
          )
      )
      dataBinomial["C3"] = dataBinomial["C3"].asfactor()
      dataBinomial["C7"] = dataBinomial["C7"].asfactor()
      dataBinomial["C8"] = dataBinomial["C8"].asfactor()
      dataBinomial["C10"] = dataBinomial["C10"].asfactor()
      dataBinomial["response"] = dataBinomial["response"].asfactor()
      frames = dataBinomial.split_frame(ratios=[0.8], seed=1234)
      trainB = frames[0]
      testB = frames[1]
      gamB = H2OGeneralizedAdditiveEstimator(
          family="binomial",
          gam_columns=gamCols,
          bs=bsT,
          scale=scaleP,
          num_knots=numKnots,
          lambda_search=True,
          seed=1,
      )
      gamB.train(
          x=myX, y=myY, training_frame=trainB, validation_frame=testB
      )
      gamPred = gamB.predict(testB)
      temp = gamPred["predict"] == testB["response"]
      gamBacc = 1 - temp.mean()[0, 0]
      rAcc = 0.01457801
      print("R accuracy: {0}, H2O accuracy: {1}.".format(rAcc, gamBacc))
      assert gamBacc <= rAcc, (
          "R mean error rate: {0}, H2O mean error rate: {1}. R performs better."
          "".format(rAcc, gamBacc)
      )
      print("Comparing H2O and R GAM performance for gaussian")
      dataGaussian = h2o.import_file(
          pyunit_utils.locate(
              "smalldata/gam_test/synthetic_20Cols_gaussian_20KRows.csv"
          )
      )
      dataGaussian["C3"] = dataGaussian["C3"].asfactor()
      dataGaussian["C7"] = dataGaussian["C7"].asfactor()
      dataGaussian["C8"] = dataGaussian["C8"].asfactor()
      dataGaussian["C10"] = dataGaussian["C10"].asfactor()
      frames = dataGaussian.split_frame(ratios=[0.8], seed=1234)
      trainB = frames[0]
      testB = frames[1]
      gamG = H2OGeneralizedAdditiveEstimator(
          family="gaussian",
          gam_columns=gamCols,
          bs=bsT,
          scale=scaleP,
          num_knots=numKnots,
          lambda_search=True,
          seed=42,
      )
      gamG.train(
          x=myX, y=myY, training_frame=trainB, validation_frame=testB
      )
      gamMSE = gamG.model_performance(valid=True).mse()
      rMSE = 0.0006933308
      print("R MSE: {0}, H2O MSE: {1}.".format(rMSE, gamMSE))
      assert (
          gamMSE <= rMSE
      ), "R MSE: {0}, H2O MSE: {1}. R performs better." "".format(
          rMSE, gamMSE
      )
  
imports: import sys
  import h2o
  from tests import pyunit_utils
  from h2o.estimators.gam import H2OGeneralizedAdditiveEstimator
label_desc: Use image node_id 1 for calling a global function with example usage: test_compare_R() without return types