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This article is about how spark mllib implements feature selection based on chi-square check. The editor thinks it is very practical, so share it with you as a reference and follow the editor to have a look.

The running code is as follows: package spark.FeatureExtractionAndTransformationimport org.apache.spark.mllib.feature.ChiSqSelectorimport org.apache.spark.mllib.linalg.Vectorsimport org.apache.spark.mllib.regression.LabeledPointimport org.apache.spark.mllib.util.MLUtilsimport org.apache.spark. {SparkConf SparkContext} / * feature selection based on Chi-square check * Chi-square check: * in statistical inference of classified data, it is generally used to test whether a sample conforms to an expected distribution. * it is the degree of deviation between the actual value of the statistical sample and the theoretical inference value. * the smaller the chi-square value. The more it tends to conform to Created by eric on 16-7-24. * / object FeatureSelection {val conf = new SparkConf () / / create the environment variable .setMaster ("local") / / set the localization handler .setAppName ("TF_IDF") / / set the name val sc = new SparkContext ( Conf) def main (args: Array [String]) {val data = MLUtils.loadLibSVMFile (sc) "/ home/eric/IdeaProjects/wordCount/src/main/spark/FeatureExtractionAndTransformation/fs.txt") val discretizedData = data.map {lp = > / / create a data processing space LabeledPoint (lp.label Vectors.dense (lp.features.toArray.map {x = > xamp 2})} val selector = new ChiSqSelector (2) / / create chi-square check val transformer = selector.fit (discretizedData) / / create training model val filteredData = discretizedData.map {lp = > / / filter the first two features LabeledPoint (lp.label, transformer.transform (lp.features))} filteredData.foreach (println) / / (0.0) [1.0, 0.5]) / / (1.0, [0.0]) / / (0.0, [1.5]) / / (1.0, [0.5]) / / (1.0) [2.0 fs.txt0 1.0])} fs.txt0 1:2 2:1 3:0 1:0 2:0 3:1 4:00 1:3 2:3 3 3 21 1 2 1 0 3 1 0 3 1 4 1 31 1 1 4 3 3 4 1 3 4 4 1 the results are as follows

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