DecisionTreeClassifier.train()调用报错

Question

我在尝试使用DecisionTreeClassifier.train()时一直提示如下错误，说当前对象不是classification包的子类，因此不能调用他的protected属性方法，但是官方文档是public类型的，因此不知道哪里出错了

Error:(218, 41) method train in class DecisionTreeClassifier cannot be accessed in org.apache.spark.ml.classification.DecisionTreeClassifier
 Access to protected method train not permitted because
 enclosing object FeatureSelection in package core is not a subclass of 
 class DecisionTreeClassifier in package classification where target is defined
        val dt = decisionTreeClassifier.train(trainRdd)

环境为scala 2.10.6 spark2.10:1.6.1 jdk1.8，相关代码如下：

import org.apache.spark.ml.classification.DecisionTreeClassifier

object FeatureSelection {
    val selectfeature=ArrayBuffer[String]()
    val tempfeature=selectfeature++ArrayBuffer(line)
    val vectorDF = new VectorAssembler()
                        .setInputCols(tempfeature.toArray)
                        .setOutputCol("features")
                        .transform(tempdf)
                        .select("label", "features")

    val Array(trainRdd, testRdd) =
        vectorDF
            .rdd
            .map(row =>  LabeledPoint(Common.any2Double(row.get(0)).get, row.getAs[Vector](1)))
            .randomSplit(Array(0.5, 0.5), 0L)

    val numClasses = 2
    val categoricalFeaturesInfo = Map[Int, Int]()

     val decisionTreeClassifier = new DecisionTreeClassifier()
          .setMaxBins(maxBins)
          .setImpurity(impurity)
          .setFeaturesCol("features")
          .setLabelCol("label")
          .setMaxDepth(maxDepth)

        val dt = decisionTreeClassifier.train(trainRdd, categoricalFeaturesInfo, numClasses)
}