所以我试图使用SparkML设置交叉验证，但出现运行时错误，说
"value setParallelism is not a member of org.apache.spark.ml.tuning.CrossValidator"
我目前正在关注spark页面教程。我对此并不陌生，因此不胜感激。贝娄是我的代码段：
import org.apache.spark.ml.{Pipeline, PipelineModel}
import org.apache.spark.ml.classification.LogisticRegression
import org.apache.spark.ml.feature.{HashingTF, Tokenizer}
import org.apache.spark.ml.linalg.Vector
import org.apache.spark.sql.Row
import org.apache.spark.ml.evaluation.BinaryClassificationEvaluator
import org.apache.spark.ml.tuning.{CrossValidator, ParamGridBuilder}
// Tokenizer
val tokenizer = new Tokenizer().setInputCol("tweet").setOutputCol("words")
// HashingTF
val hash_tf = new HashingTF().setInputCol(tokenizer.getOutputCol).setOutputCol("features")
// ML models
val l_regression = new LogisticRegression().setMaxIter(100).setRegParam(0.15)
// Pipeline
val pipe = new Pipeline().setStages(Array(tokenizer, hash_tf, l_regression))
val paramGrid = new ParamGridBuilder()
.addGrid(hash_tf.numFeatures, Array(10,100,1000))
.addGrid(l_regression.regParam, Array(0.1,0.01,0.001))
.build()
val c_validator = new CrossValidator()
.setEstimator(pipe)
.setEvaluator(new BinaryClassificationEvaluator)
.setEstimatorParamMaps(paramGrid)
.setNumFolds(3)
.setParallelism(2)
最佳答案
setParallelism is available only in Spark 2.3 or later。您必须使用早期版本：
（仅限专家）参数设置器
（...）
def setParallelism(value: Int): CrossValidator.this.type
设置最大并行度，以并行评估模型。串行评估的默认值为1
注释@Since（“ 2.3.0”）
关于scala - sparkml setParallelism用于交叉验证，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/49970460/