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我正在尝试执行随机森林分类器并使用交叉验证评估模型。我与pySpark合作。输入的CSV文件将以Spark DataFrame格式加载。但是在构建模型时我遇到了一个问题。

下面是代码。

from pyspark import SparkContext
from pyspark.sql import SQLContext
from pyspark.ml import Pipeline
from pyspark.ml.classification import RandomForestClassifier
from pyspark.ml.tuning import CrossValidator, ParamGridBuilder
from pyspark.ml.evaluation import MulticlassClassificationEvaluator
from pyspark.mllib.evaluation import BinaryClassificationMetrics
sc = SparkContext()
sqlContext = SQLContext(sc)
trainingData =(sqlContext.read
         .format("com.databricks.spark.csv")
         .option("header", "true")
         .option("inferSchema", "true")
         .load("/PATH/CSVFile"))
numFolds = 10 
rf = RandomForestClassifier(numTrees=100, maxDepth=5, maxBins=5, labelCol="V5409",featuresCol="features",seed=42)
evaluator = MulticlassClassificationEvaluator().setLabelCol("V5409").setPredictionCol("prediction").setMetricName("accuracy")
paramGrid = ParamGridBuilder().build()

pipeline = Pipeline(stages=[rf])
paramGrid=ParamGridBuilder().build()
crossval = CrossValidator(
    estimator=pipeline,
    estimatorParamMaps=paramGrid,
    evaluator=evaluator,
    numFolds=numFolds)
model = crossval.fit(trainingData)
print accuracy

我低于错误

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