{"title":"DeltaPy: Python中表格数据增强的框架","authors":"Derek Snow","doi":"10.2139/ssrn.3582219","DOIUrl":null,"url":null,"abstract":"A range of data abstractions have come to the fore since the re-emergence of machine learning. This includes procedures like feature engineering, extraction, transformation, and selection, as well as data pre-processing, generation, synthesisation, and augmentation. This report attempts to unify some of this terminology with the development of a bare-bones Python package, DeltaPy.","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"31 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2020-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"DeltaPy: A Framework for Tabular Data Augmentation in Python\",\"authors\":\"Derek Snow\",\"doi\":\"10.2139/ssrn.3582219\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A range of data abstractions have come to the fore since the re-emergence of machine learning. This includes procedures like feature engineering, extraction, transformation, and selection, as well as data pre-processing, generation, synthesisation, and augmentation. This report attempts to unify some of this terminology with the development of a bare-bones Python package, DeltaPy.\",\"PeriodicalId\":11495,\"journal\":{\"name\":\"Econometric Modeling: Capital Markets - Forecasting eJournal\",\"volume\":\"31 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-04-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Econometric Modeling: Capital Markets - Forecasting eJournal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2139/ssrn.3582219\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Econometric Modeling: Capital Markets - Forecasting eJournal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2139/ssrn.3582219","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
DeltaPy: A Framework for Tabular Data Augmentation in Python
A range of data abstractions have come to the fore since the re-emergence of machine learning. This includes procedures like feature engineering, extraction, transformation, and selection, as well as data pre-processing, generation, synthesisation, and augmentation. This report attempts to unify some of this terminology with the development of a bare-bones Python package, DeltaPy.
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