[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"branding":3,"analytics":7,"article-tree-ensembles-still-hold-their-own-against-deep-learning":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},6651,"tree-ensembles-still-hold-their-own-against-deep-learning","Tree Ensembles Still Hold Their Own Against Deep Learning","A new benchmark finds tabular deep learning models can match classical machine learning on urban land cover classification, but only under specific conditions.","Deep learning still has to earn its keep against boring old gradient boosting - at least for classifying city land from aerial photos.\n\nResearchers benchmarked classical machine learning models (Logistic Regression, SVM, Random Forest, XGBoost, CatBoost) against newer tabular deep learning models (TabNet, FT-Transformer, TabTransformer, TabSeq, and 1D CNNs) on the UCI Urban Land Cover dataset. The data sorts pixels from high-resolution aerial imagery into nine classes, like roads, trees, grass, and water. It is a messy dataset by design, with high dimensionality, mismatched feature types, and uneven class sizes. The team used weighted cross-entropy loss to help the deep learning models cope with that imbalance, then compared everyone on accuracy, precision, recall, F1, and AUC-ROC.\n\nThe finding: tree ensembles are still solid default choices, but they are not automatically the best choice. Tabular deep learning models matched or beat them when the data's patterns were non-linear and the imbalance handling actually worked.\n\nThat is a narrower claim than the deep learning hype cycle usually makes, and it matches what practitioners already suspected - on structured, spreadsheet-like data, boosted trees remain stubbornly hard to beat without real engineering effort on the neural net side. Urban planners and remote sensing teams picking a model for land cover maps should read this as permission to keep using XGBoost by default, not a mandate to switch.","[\"machine learning\",\"deep learning\",\"remote sensing\",\"urban planning\"]","2026-09-17T04:00:00.000Z","2026-09-18T04:25:21.642Z","2026-09-18T04:25:33.515Z","published",null,[],"ai",[26,27,28,29],"machine learning","deep learning","remote sensing","urban planning",[31],{"name":32,"url":33},"arXiv cs.AI","https:\u002F\u002Farxiv.org\u002Fabs\u002F2609.19010",0,{"sections":36},[37,41,45,50,55,59,63,68,73,77,82,87,92,97],{"name":38,"slug":24,"count":39,"latest_published_at":40},"AI",3852,"2026-09-17T08:27:09.000Z",{"name":42,"slug":43,"count":44,"latest_published_at":18},"Security","security",648,{"name":46,"slug":47,"count":48,"latest_published_at":49},"Policy","policy",338,"2026-09-11T04:00:00.000Z",{"name":51,"slug":52,"count":53,"latest_published_at":54},"Deals","deals",179,"2026-06-29T20:02:07.000Z",{"name":56,"slug":57,"count":58,"latest_published_at":18},"Hardware","hardware",154,{"name":60,"slug":61,"count":62,"latest_published_at":18},"Science","science",114,{"name":64,"slug":65,"count":66,"latest_published_at":67},"Consumer Tech","consumer-tech",99,"2026-09-09T17:27:33.000Z",{"name":69,"slug":70,"count":71,"latest_published_at":72},"Software","software",75,"2026-09-10T20:41:21.000Z",{"name":74,"slug":75,"count":76,"latest_published_at":18},"Dev Tools","dev-tools",73,{"name":78,"slug":79,"count":80,"latest_published_at":81},"Startups","startups",55,"2026-09-09T23:14:29.000Z",{"name":83,"slug":84,"count":85,"latest_published_at":86},"Gaming","gaming",43,"2026-09-10T12:18:06.000Z",{"name":88,"slug":89,"count":90,"latest_published_at":91},"General","general",41,"2026-09-08T01:57:23.000Z",{"name":93,"slug":94,"count":95,"latest_published_at":96},"Reviews","reviews",20,"2026-06-24T12:00:01.000Z",{"name":98,"slug":99,"count":100,"latest_published_at":101},"How-To","how-to",6,"2026-06-16T09:00:00.000Z"]