[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"branding":3,"analytics":7,"article-no-training-models-beat-tuned-xgboost-on-every-dataset":10,"sections":40},{"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":30,"tags":31,"sources":35,"feedback":39,"feedback_at":22,"cost_usd":39,"total_tokens":39},8169,"no-training-models-beat-tuned-xgboost-on-every-dataset","No-training models beat tuned XGBoost on every dataset","Two no-training tabular foundation models, TabPFN and TabICL, beat a properly tuned XGBoost across all 14 datasets in a new independent benchmark.","A new independent benchmark says you can skip training a model entirely and still beat XGBoost on tabular data.\n\nThe comparison, published on a personal blog and surfaced on Hacker News, tests two tabular foundation models, TabPFN and TabICL, against XGBoost, the gradient-boosting library most practitioners reach for on structured data. TabPFN and TabICL skip the training step entirely: instead of fitting a model to your dataset, they take the data as direct input and produce predictions immediately. The author tuned the XGBoost baseline rather than leaving it at default settings, a deliberate choice to make the fight harder. Across all 14 datasets in the test, both no-training models came out ahead of that tuned XGBoost.\n\nThe tuned baseline is the detail that makes this notable. Plenty of foundation-model benchmarks only ever beat a lazy, default-settings XGBoost, which is an easy target and an easy result to dismiss. Beating a properly tuned one is a harder bar to clear, and it raises a real question for teams that currently budget engineering time on hyperparameter search and retraining pipelines.\n\nA 14-0 sweep against a tuned baseline is a real result, not a rigged demo, and it's a fair reason to test TabPFN and TabICL against your own tabular data before reaching for XGBoost by default.","[\"ai\",\"machine-learning\",\"xgboost\",\"tabular-data\"]","2026-09-28T02:27:40.000Z","2026-09-28T12:25:24.221Z","2026-09-28T12:25:30.989Z","published",null,[24],{"id":25,"reviewer":26,"round":27,"reason":28,"status":29},"editor-r1","editor",1,"Fix the closing paragraph, which hedges into 'not worth ripping out your pipelines yet' and undercuts the confident 14\u002F14 headline claim instead of landing a conclusion, and resolve the inconsistency between a singular 'A No-Training Model' headline and the body's claim that two distinct models (TabPFN and TabICL) each won all 14 datasets — clarify which model(s) actually achieved which results, and only include specifics (like XGBoost being tuned rather than default) that are actually verifiabl","resolved","ai",[30,32,33,34],"machine-learning","xgboost","tabular-data",[36],{"name":37,"url":38},"Hacker News","https:\u002F\u002Fefraingaray.com\u002Fen\u002Fblog\u002Ftabpfn-vs-xgboost\u002F",0,{"sections":41},[42,46,50,55,60,65,69,74,79,84,89,94,98,103],{"name":43,"slug":30,"count":44,"latest_published_at":45},"AI",4798,"2026-09-28T04:00:00.000Z",{"name":47,"slug":48,"count":49,"latest_published_at":45},"Security","security",762,{"name":51,"slug":52,"count":53,"latest_published_at":54},"Policy","policy",399,"2026-09-27T18:39:02.000Z",{"name":56,"slug":57,"count":58,"latest_published_at":59},"Deals","deals",261,"2026-09-27T15:30:35.000Z",{"name":61,"slug":62,"count":63,"latest_published_at":64},"Hardware","hardware",188,"2026-09-27T20:46:36.000Z",{"name":66,"slug":67,"count":68,"latest_published_at":45},"Science","science",151,{"name":70,"slug":71,"count":72,"latest_published_at":73},"Consumer Tech","consumer-tech",135,"2026-09-26T14:30:00.000Z",{"name":75,"slug":76,"count":77,"latest_published_at":78},"Software","software",91,"2026-09-25T20:55:00.000Z",{"name":80,"slug":81,"count":82,"latest_published_at":83},"Dev Tools","dev-tools",84,"2026-09-26T04:20:58.000Z",{"name":85,"slug":86,"count":87,"latest_published_at":88},"Startups","startups",76,"2026-09-25T18:33:59.000Z",{"name":90,"slug":91,"count":92,"latest_published_at":93},"Gaming","gaming",48,"2026-09-25T18:35:21.000Z",{"name":95,"slug":96,"count":92,"latest_published_at":97},"General","general","2026-09-26T17:02:42.000Z",{"name":99,"slug":100,"count":101,"latest_published_at":102},"Reviews","reviews",30,"2026-09-24T20:07:31.000Z",{"name":104,"slug":105,"count":106,"latest_published_at":107},"How-To","how-to",6,"2026-06-16T09:00:00.000Z"]