Xeetu ensemble ak yokk degrade
Pexem ensemble dafay boole ay model yu yomb yu bari suko defee groupe bi gëna mëna wax luy am ci benn model.
Résumé
Gradient boosting is the most powerful of these — it builds trees one at a time, each correcting the errors of the last, and dominates real-world tabular machine learning.
Plongeur bu xóot
Ensemble yi dañu sukkandikoo ci xalaat bu yomb: jàngat yu bari yu néew doole, boole leen, mën nañu nekk benn bu am doole. Ñaari njaboot ñoo jiite. Bagging (lu melni, garabi Random) dafay tàggat garab yu bari ci paralel ci misaal yu bari ba noppi di leen moyenne, loolu dafay wàññi variance. Boosting saxaar models yu toppalante, bu nekk focus ci njuumte yi njëkka def, lu gëna wàññi biais. Gradient boosting dafay kaadre garab gu bees gu nekk ni jéego bu méngoo ak gradient bu baaxul bi - njuumte yi des - ci fonction perte bi fi ñu tolli nii. Biblioteek yu melni XGBoost, LightGBM, ak CatBoost dañuy yokk yamale, xaaj bu am xel, ak pexe yu gaaw. Ci done yuñ yamale / tablo - gis njuuj njaaj, njëg, rang - xeetu xam-xam yooyu dañuy faral di daan jàng bu xóot bi ba noppi jël ndam li gëna bari ci joŋante Kaggle.
Gis-gis xarala
Ci yokk gradient, dangay tàmbalee ci xam-xam bu amul benn njariñ ba noppi nga yokk garab gu ndaw gu méngoo ak residu yi - gradient bi ñàkk ci wàllu xam-xam yi fi nekk. Li garab gu nekk mëna def mingi aju ci ni muy jàngee (réduction), suko defee model bi di gëna mëna dem ci jéego yu ndaw. Ndax njuumte yi dañuy gëna yokk sudee dangay gëna méngoo, yamale (àppu xóotaayu garab, rang yu ndaw ak màndarga yi, L1 / L2 daan ci diisaayu xob yi) dafay am solo ngir moytu ensemble bi xam bruit.
njeextalu pexe
dogal yu gëna leer
Daf lay jàppale nga tàqale kàddu yu leer ci wàllu xarala ak làkku fësal njaay.
Njëgg ak budget
Mën nga laaj laaj yu gëna baax ci samp gi balaa ngay dugal xaalis wala sa jotu liggéey.
Ekip ak def liggéey
Ekip yi bokk xam-xam ñoo gëna mëna jël yenn dogal ci wàllu produit, politik ak jàng.
Ëlëgu njuréefi Ensemble ak yokk degrade
Garab yu am gradient-boosted ñu ngi des ci default ngir done tabular te wane wuñu benn màndarga ni dañu leen dindi foofu, doonte jàng bu xóot bi dafa jëm ca kanam feneen. Xaarandil yokkute yu wéy ci gaawaay ak gaawaayu GPU, gëna mëna jëfandikoo ay done yu kategorik ak yu ñàkk, ak boole bu gëna dëgër ak masin buy jàng otomatik (AutoML) pipelines. Gëstu ci boole boosting ak reso neuronal, ak ci anam yu gëna gaaw, gëna mëna tekki, mingi am. Ngir ñiy jëfandikoo, yokk bibliotek yi dina ñu nekk tànneef bu wóor, gëna jubal ci jafe-jafe yi am formu këyitu xayma.
Doxal ci àdduna dëgg
Bànk yi ak ñiy jëfandikoo XGBoost ngir màndargaal jëflante yu baaxul yi ci man-mani tablo yu melni dayo, barab, ak waxtu.
Motëri seetlu yi ak bitik yi ci net bi dañuy rang resultaa yi ci xeetu 'jàng-ba-rang' buñ yokk ci gradient bi.
Assurance yi ak entreprise yiy lebal xaalis dañuy seetlu risk yi ak tëral njëg yi ci done yuñ tëral ci kiliyaan yi.
Konkurër Kaggle yi jël nañu ndam ci joŋante done tablo yi ci boole xeetu LightGBM ak CatBoost.
Risk yi ak balustrade yi
Ekip yu bari mën nañu jëfandikoo benn baat ci anam wu wuute, kon teela leeral yaatuwaayam.
Benchmark yi mën nañu nuru lu am doole waaye performance yi ci àdduna bi duñu tolloo.
Bëgg kalite done ak palaŋu jàngat dafay faral di jur njariñ yu yomba dagg.
Roadmap ngir samp gi
Tàmbaleel ci joxe leeral ci làkk wu leer ci njariñ li nga soxla.
Tannal benn metric bu baax ak benn anam bu baaxul balaa ngay saytu.
Doxal ab pilote bu ndaw ak ay done yu representatif, du ab demo bu leer.
Bindal fi pexe Ensemble ak Gradient Boosting di jàppale ak fi pexe yu gëna yomba gëna baax.
Weyal di banneexu
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Gis bi ci topp
Wàcci degrade stokastik ak saa
Laaj yi ñuy faral di laaj
What is Ensemble Methods and Gradient Boosting?
Pexem ensemble dafay boole ay model yu yomb yu bari suko defee groupe bi gëna mëna wax luy am ci benn model. Gradient boosting mooy li gëna am doole ci ñoom - dafay tabax garab benn-benn, bu nekk di seet njuumti yi ci mujj, ba noppi di ëpp doole ci jàngu masin tabular ci àdduna dëgg.
Lan mooy xalaat bu mag bi ci ginaaw pexe ensemble?
Ensembles yi dañuy boole li ñuy wax ci model yu bari, suko defee seen génne buñ boole mu gëna dëggu ak dëgër yeneen nit ñi.
Lan moo wuutale yokkuteg gradient ak sac (lu melni, àll yu bari)?
Bagging dafay tabax ay model yu moom seen bopp ci paralel ba noppi di leen moyenne (di wàññi variance), boosting dafay tabax ay model benn ci ginaaw beneen, ku nekk di defar njuumti yi ci mujj (di wàññi bias).
Ci yokk degrade, garab gu bees gu nekk mën na xayma lan?
Garab gu nekk dafay méngoo ak gradient bu baaxul bi ci ñàkk - lu gëna bari ci njuumte yi des - kon yokk ko nudges predictions ci valeur yu dëggu yi.
Luy njariñu taxawaayu jàng (réduction) ci yokk?
Tolluwaayu jàng bu ndaw dafay wàññi yeesali garab gu nekk, loolu mooy gëna yombal généralisation ci njëgu soxla garab yu bari.
Ban xeetu done la garab yu am gradient di gëna am doole?
Bibliothèque yu melni XGBoost ak LightGBM dañuy gëna am doole ci done tablo te dañuy jël ndam li ci joŋante tablo Kaggle yu bari.