如何使用 Spark LinearSVC 模型获得最佳功能？

Question

我正在尝试使用 ChiSqSelector 来确定 Spark 2.2 LSVCModel 的最佳特征，因此：

import org.apache.spark.ml.feature.ChiSqSelector
val chiSelector = new ChiSqSelector().setNumTopFeatures(5).
   setFeaturesCol("features").
   setLabelCol("label").setOutputCol("selectedFeatures")

val pipeline = new Pipeline().setStages(Array(labelIndexer, monthIndexer, hashingTF
   , idf, va, featureIndexer,  chiSelector, lsvc, labelConverter))

val model = pipeline.fit(training)
val importantFeatures = model.selectedFeatures

import org.apache.spark.ml.classification.LinearSVCModel
val LSVCModel= model.stages(6).asInstanceOf[org.apache.spark.ml.classification.
   LinearSVCModel]

val importantFeatures = LSVCModel.selectedFeatures

给出错误：

<console>:180: error: value selectedFeatures is not a member of 
org.apache.spark.ml.classification.LinearSVCModel
   val importantFeatures = LSVCModel.selectedFeatures

这个模型可以使用 ChiSqSelector 吗？如果没有，还有其他选择吗？

Answer 1

Linear SVC 不会做任何特征选择。您应该从管道中提取 ChiSqSelectorModel，而不是 LinearSVCModel。

import org.apache.spark.ml.feature.ChiSqSelectorModel
val chiSqModel = model.stages(6).asInstanceOf[ChiSqSelectorModel]

val importantFeatures = chiSqModel.selectedFeatures

如何使用 Spark LinearSVC 模型获得最佳功能？

Ho do get best features with Spark LinearSVC model?

machine-learning

feature-selection

apache-spark