Regularized Decision Tree (Pruned and Constrained CART)
Mti wa uamuzi uliokua bila vizuizi utahifadhi data ya mafunzo, ukigawanyika hadi kila jani liwe na uchunguzi mmoja tu. Uwekaji kawaida hufanya kama kizuizi kinachoambia mti kusimama wakati kuongeza mgawanyo mwingine kunagharimu zaidi katika ugumu kuliko faida inayopata katika kufaa. Aina ya kawaida zaidi, upunguzaji wa gharama-ugumu, hufanya kazi kwa kurudi nyuma: kwanza ukue mti kamili, kisha upunguze matawi ambayo kuondolewa kwake husababisha ongezeko dogo tu katika makosa ya mafunzo, ikiongozwa na kigezo cha ugumu alpha kilichorekebishwa kupitia uthibitisho wa pande zote. Vizuizi rahisi vya kimuundo - kina cha juu, sampuli za chini kwa kila jani - hufikia athari sawa moja kwa moja na ni rahisi kurekebishwa.
Soma mbinu kamili
Ingia kwa akaunti ya bure ili kusoma sehemu hii.
Method map
The neighbourhood of related methods — select a node to explore.
Vyanzo
- Breiman, L., Friedman, J., Olshen, R., & Stone, C. (1984). Classification and Regression Trees. Wadsworth. ISBN: 978-0-412-04841-8
- Esposito, F., Malerba, D., & Semeraro, G. (1997). A comparative analysis of methods for pruning decision trees. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(5), 476–491. DOI: 10.1109/34.589207 ↗
Jinsi ya kunukuu ukurasa huu
ScholarGate. (2026, June 3). Regularized Decision Tree (Pruned and Constrained CART). ScholarGate. https://scholargate.app/sw/machine-learning/regularized-decision-tree
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- KuimarishaUjifunzaji wa Mashine↔ compare
- Mti wa UamuziUjifunzaji wa Mashine↔ compare
- Miti ya ZiadaUjifunzaji wa Mashine↔ compare
- Msitu NasibuUjifunzaji wa Mashine↔ compare
- Urejeshaji Linear UliodhibitiwaUjifunzaji wa Mashine↔ compare
- Msitu wa Kawaida wa BahatishaUjifunzaji wa Mashine↔ compare
Imerejelewa na
Umeona tatizo kwenye ukurasa huu? Ripoti au pendekeza marekebisho →