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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.

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Vyanzo

  1. Breiman, L., Friedman, J., Olshen, R., & Stone, C. (1984). Classification and Regression Trees. Wadsworth. ISBN: 978-0-412-04841-8
  2. 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

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ScholarGateRegularized Decision Tree (Regularized Decision Tree (Pruned and Constrained CART)). Imepatikana 2026-06-15 kutoka https://scholargate.app/sw/machine-learning/regularized-decision-tree · Seti ya data: https://doi.org/10.5281/zenodo.20539026