KUCHAYTIRISH USULLARI
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Abstrak:
Ushbu tezis sun’iy intellekt va mashinaviy o‘qitishda keng qo‘llaniladigan kuchaytirish usullari ning nazariy asoslari, turlari va ularning amaliy qo‘llanilishi haqida ma’lumot beradi. Kuchaytirish usullari bir nechta mustaqil modellarni birlashtirish orqali yagona, ancha aniq va barqaror prognoz beruvchi model yaratishga asoslanadi. Bu yondashuv alohida modelning kamchiliklarini bartaraf etib, umumiy natijaning aniqligini oshirishga xizmat qiladi. Ishda ansambllarning asosiy turlari - bagging, boosting, stacking, shuningdek Random Forest, AdaBoost, Gradient Boosting kabi zamonaviy algoritmlar ko‘rib chiqiladi. Har bir usulning afzalliklari, cheklovlari va qaysi vazifalarda samarali ekani amaliy misollar orqali yoritiladi. Kuchaytirish usullarining tasniflashi va ishlash mexanizmi o‘rganilar ekan, ular sun’iy intellektning turli sohalarida - klassifikatsiya, regressiya, anomaliyalarni aniqlash va bashoratlash kabi jarayonlarda eng yuqori natijalarni berishi ta’kidlanadi. Ushbu ish kuchaytirish usullarining mohiyatini, ularning ahamiyatini va real muammolarni hal etishdagi rolini tushuntirishga qaratilgan.
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