[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"branding":3,"analytics":7,"article-new-method-trains-deeper-decision-trees-without-losing-accuracy":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},8983,"new-method-trains-deeper-decision-trees-without-losing-accuracy","New Method Trains Deeper Decision Trees Without Losing Accuracy","A new algorithm builds deep, accurate decision trees on large datasets, closing the gap between fast-but-sloppy heuristics and slow-but-optimal solvers.","Researchers have found a faster way to grow decision trees that stay both deep and accurate.\n\nThe method, described in a paper posted to arXiv, tackles a well-known trade-off in building decision trees. Methods that guarantee the mathematically best tree tend to work only on small, shallow trees with features chopped into yes-or-no splits. Faster heuristic methods can handle bigger, deeper trees and continuous-valued features, but they settle for good-enough predictions rather than the best possible one. This approach splits the difference: it solves the top of the tree with an exact search technique called branch-and-reduce, approximates the rest with quick greedy heuristics similar to a lookahead rollout in reinforcement learning, then loops back over the whole tree in a low-cost \"moving-horizon\" pass to sharpen accuracy.\n\nDecision trees matter because, unlike neural networks, a person can actually read the rules a tree uses to make a decision, which counts for something in fields like healthcare or lending where regulators want an explanation, not a black box. The bottleneck has always been that the most interpretable, provably-optimal trees don't scale past small, shallow problems, forcing practitioners to choose between trustworthy-but-shallow models and accurate-but-opaque ones. If this approach holds up outside the paper's own benchmarks, it narrows that gap without demanding a bigger compute budget.\n\nIt's still an arXiv preprint, not a peer-reviewed benchmark war, so treat \"near-optimal\" as a claim worth retesting on your own data before swapping out your gradient-boosted forest.","[\"decision-trees\",\"machine-learning\",\"interpretability\",\"optimization\"]","2026-10-01T04:00:00.000Z","2026-10-01T14:14:11.420Z","2026-10-01T14:14:15.345Z","published",null,[],"ai",[26,27,28,29],"decision-trees","machine-learning","interpretability","optimization",[31],{"name":32,"url":33},"arXiv cs.AI","https:\u002F\u002Farxiv.org\u002Fabs\u002F2609.38194",0,{"sections":36},[37,40,44,49,54,59,63,68,73,77,82,87,92,97],{"name":38,"slug":24,"count":39,"latest_published_at":18},"AI",5453,{"name":41,"slug":42,"count":43,"latest_published_at":18},"Security","security",805,{"name":45,"slug":46,"count":47,"latest_published_at":48},"Policy","policy",429,"2026-10-01T02:26:17.000Z",{"name":50,"slug":51,"count":52,"latest_published_at":53},"Deals","deals",298,"2026-09-30T21:00:26.000Z",{"name":55,"slug":56,"count":57,"latest_published_at":58},"Hardware","hardware",196,"2026-09-30T13:00:00.000Z",{"name":60,"slug":61,"count":62,"latest_published_at":18},"Science","science",159,{"name":64,"slug":65,"count":66,"latest_published_at":67},"Consumer Tech","consumer-tech",149,"2026-09-30T22:57:11.000Z",{"name":69,"slug":70,"count":71,"latest_published_at":72},"Dev Tools","dev-tools",93,"2026-10-01T02:30:48.000Z",{"name":74,"slug":75,"count":71,"latest_published_at":76},"Software","software","2026-09-30T21:41:11.000Z",{"name":78,"slug":79,"count":80,"latest_published_at":81},"Startups","startups",84,"2026-09-30T20:39:09.000Z",{"name":83,"slug":84,"count":85,"latest_published_at":86},"Gaming","gaming",51,"2026-09-30T16:24:30.000Z",{"name":88,"slug":89,"count":90,"latest_published_at":91},"General","general",50,"2026-09-30T21:37:54.000Z",{"name":93,"slug":94,"count":95,"latest_published_at":96},"Reviews","reviews",31,"2026-09-28T14:31:34.000Z",{"name":98,"slug":99,"count":100,"latest_published_at":101},"How-To","how-to",6,"2026-06-16T09:00:00.000Z"]