code: def test_XGBClassifier_train(self):
      from sys import platform
  
      if platform in ("darwin", "win32"):
          return
      path = os.path.join(self.resource_path, "xgbclassifier/")
      modelPath = path + "XGBClassifer.bin"
      filePath = path + "test.csv"
  
      df = self.sqlContext.read.csv(
          filePath, sep=",", inferSchema=True, header=True
      )
      df = df.select(
          array("age", "gender", "jointime", "star").alias("features"),
          "label",
      ).withColumn(
          "features",
          udf(lambda x: DenseVector(x), VectorUDT())("features"),
      )
      params = {
          "eta": 0.2,
          "max_depth": 4,
          "max_leaf_nodes": 8,
          "objective": "binary:logistic",
          "num_round": 100,
      }
      classifier = XGBClassifier(params)
      xgbmodel = classifier.fit(df)
      xgbmodel.setFeaturesCol("features")
      predicts = xgbmodel.transform(df)
      assert predicts.count() == 14
  
file_path: BigDL/python/dllib/test/bigdl/nnframes/test_treemodel.py
imports: import pytest
  from bigdl.dllib.nncontext import *
  from bigdl.dllib.nnframes.tree_model import *
  from bigdl.dllib.utils.tf import *
  from pyspark.ml.linalg import DenseVector, VectorUDT
  from pyspark.sql.functions import udf, array
label_desc: Use image node_id 4 for calling the TestTreeModel obj's underlying member method code with example usage: obj.test_XGBClassifier_train() without return types