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AFE在RTG上的应用
Abstract
AutomaticFeatureEngineering(AFE)isanimportanttechniqueinmachinelearninganddatascience.Ithelpstoautomaticallyextractrelevantfeaturesfromrawdata,whichcanbeusedinbuildingpredictivemodels.Inrecentyears,theuseofAFEhasbecomemorepopular,especiallyinthefieldofdeeplearning.Inthispaper,wediscusstheapplicationofAFEonRandomTreeGenerator(RTG)andhowithelpstoimprovetheaccuracyofthemodel.
Introduction
RandomTreeGenerator(RTG)isamachinelearningmodelthatisusedforclassificationandregressiontasks.RTGusesdecisiontreealgorithmstogeneratetreesthatcanmakepredictionsbasedoninputdata.However,theaccuracyofRTGdependsonthequalityofthefeaturesthatareusedtotrainthemodel.Inmanycases,thequalityofthefeaturesisnotsufficient,whichcanleadtopoorperformanceofthemodel.
OnewaytoimprovethequalityofthefeaturesistouseAutomaticFeatureEngineering(AFE).AFEistheprocessofautomaticallyextractingrelevantfeaturesfromrawdata.AFEcanhelptoidentifypatternsandrelationshipsinthedatathatarenotobvioustohumans.Thiscanbeespeciallyusefulincaseswheretherearealargenumberoffeaturesanditisdifficulttomanuallyidentifywhichfeaturesarerelevant.
Inthispaper,wediscusstheapplicationofAFEonRTGandhowithelpstoimprovetheaccuracyofthemodel.
TheoreticalBackground
RandomTreeGenerator(RTG)
RandomTreeGenerator(RTG)isamachinelearningmodelthatisusedforclassificationandregressiontasks.RTGisbasedondecisiontreealgorithms,whichuseasetofrulestomakepredictionsbasedoninputdata.Thenodesofthetreerepresentadecisionbasedonafeatureofthedata,andthebranchesrepresentthepossibleoutcomesofthatdecision.
RTGbuildsasetoftreesbyrandomlyselectingasubsetofthefeaturesandasubsetofthedatatotraineachtree.Thisprocesshelpstoreduceoverfittingandimprovetheaccuracyofthemodel.Thefinalpredictionismadebycombiningthepredictionsofallthetreesintheset.
AutomaticFeatureEngineering(AFE)
AutomaticFeatureEngineering(AFE)istheprocessofautomaticallyextractingrelevantfeaturesfromrawdata.AFEalgorithmsuseavarietyoftechniques,suchasstatisticalanalysis,correlationanalysis,andcl