[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"branding":3,"analytics":7,"article-llms-that-write-their-own-data-prep-code":10,"sections":35},{"siteName":4,"siteTagline":5,"publisherName":4,"contactEmail":6},"The Revision","Tech news, decoded.","editor@therevision.news",{"gaMeasurementId":8,"adsenseClientId":9},"G-ZW2MV82GYR","ca-pub-8533917693782264",{"article":11},{"id":12,"slug":13,"title":14,"dek":15,"body_md":16,"tags_json":17,"published_at":18,"created_at":19,"updated_at":20,"status":21,"review_note":22,"review_notes":23,"image_url":22,"persona_id":22,"persona_name":22,"section":24,"tags":25,"sources":30,"feedback":34,"feedback_at":22,"cost_usd":34,"total_tokens":34},3483,"llms-that-write-their-own-data-prep-code","LLMs That Write Their Own Data Prep Code","A new framework called EFE uses language models to evolve Python preprocessing programs, cutting forecasting errors by up to 19% on some datasets.","A research framework is using language models to automatically discover and refine the data-cleaning steps that typically require the most human labor in a machine learning project.\n\nEvolutionary Feature Engineering, or EFE, treats data preprocessing as a search problem. It generates candidate Python programs with a standard fit\u002Ftransform interface, runs them against real datasets, and uses validation performance as feedback to evolve better versions — all without a human in the loop. The researchers tested two variants: EFE-Time, aimed at time-series forecasting, and EFE-Tab, which targets tabular prediction tasks. EFE-Time reduced forecasting errors by 3% or more on average across datasets and by as much as 19% on one COVID mortality dataset, working on top of existing time-series foundation models including Chronos-2.\n\nThe harder sell here is interpretability. Most automated feature engineering produces black-box transforms that accuracy-focused practitioners accept and everyone else ignores. EFE-Tab, by contrast, generates compact, readable feature programs — and the paper reports it was especially effective on classical decision trees, where a small set of evolved features matched or beat competing LLM-based methods while keeping the model explainable. That matters for any domain where a regulator or a skeptical colleague asks why the model did what it did.\n\nFeature engineering automation has been a recurring promise in AutoML for a decade, with tools like Featuretools and various AutoML platforms making similar claims. What EFE adds is the generative flexibility of a language model as the mutation operator, replacing hand-coded transformation libraries with open-ended code synthesis — a bet that LLMs are better at creative search than at reliable reasoning.","[\"machine learning\",\"llm\",\"automl\",\"data science\"]","2026-07-03T04:00:00.000Z","2026-07-03T07:01:57.558Z","2026-07-03T07:02:00.675Z","published",null,[],"ai",[26,27,28,29],"machine learning","llm","automl","data science",[31],{"name":32,"url":33},"arXiv cs.AI","https:\u002F\u002Farxiv.org\u002Fabs\u002F2607.01548",0,{"sections":36},[37,41,46,51,56,61,66,71,76,81,86,90,95,100],{"name":38,"slug":24,"count":39,"latest_published_at":40},"AI",2590,"2026-07-16T04:00:00.000Z",{"name":42,"slug":43,"count":44,"latest_published_at":45},"Security","security",294,"2026-07-15T19:59:48.000Z",{"name":47,"slug":48,"count":49,"latest_published_at":50},"Deals","deals",179,"2026-06-29T20:02:07.000Z",{"name":52,"slug":53,"count":54,"latest_published_at":55},"Policy","policy",158,"2026-07-16T00:02:48.000Z",{"name":57,"slug":58,"count":59,"latest_published_at":60},"Hardware","hardware",122,"2026-07-14T19:46:26.000Z",{"name":62,"slug":63,"count":64,"latest_published_at":65},"Consumer Tech","consumer-tech",93,"2026-07-13T13:20:48.000Z",{"name":67,"slug":68,"count":69,"latest_published_at":70},"Software","software",70,"2026-07-13T19:52:25.000Z",{"name":72,"slug":73,"count":74,"latest_published_at":75},"Science","science",66,"2026-07-10T10:29:37.000Z",{"name":77,"slug":78,"count":79,"latest_published_at":80},"Dev Tools","dev-tools",59,"2026-07-07T04:00:00.000Z",{"name":82,"slug":83,"count":84,"latest_published_at":85},"Gaming","gaming",41,"2026-07-09T04:00:00.000Z",{"name":87,"slug":88,"count":84,"latest_published_at":89},"Startups","startups","2026-06-29T20:55:50.000Z",{"name":91,"slug":92,"count":93,"latest_published_at":94},"General","general",29,"2026-07-10T22:28:58.000Z",{"name":96,"slug":97,"count":98,"latest_published_at":99},"Reviews","reviews",20,"2026-06-24T12:00:01.000Z",{"name":101,"slug":102,"count":103,"latest_published_at":104},"How-To","how-to",6,"2026-06-16T09:00:00.000Z"]