Vol. 5 No. 2 (2026), Articles
Vol. 5 No. 2 (2026)

ML ALGORITMLARINI TAQQOSLASH: O'QUVCHI O'ZLASHTIRISH DARAJASINI BASHORATLASHDA XGBOOST VA LOGISTIC REGRESSION SAMARADORLIGI

Articles
Published 2026-06-20
Yunusxo'jayev Lutfullo Zafarxo'ja o'g'li
Magistratura talabasi
Jalolov Tursunbek Sadriddinovich
p.f.f.d. (PhD), dotsent Osiyo xalqaro universiteti
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Keywords

XGBoost, Logistic Regression, qiyosiy tahlil, o'quvchi o'zlashtirishi, mashinaviy o'qitish, Learning Analytics, ta'lim texnologiyalari.

Abstract

Ushbu maqolada o'quvchilarning akademik o'zlashtirish darajasini bashoratlashda XGBoost va Logistic Regression algoritmlarining qiyosiy tahlili amalga oshirilgan. Tadqiqot davomida 750 nafar o'quvchining 18 ta belgidan iborat ma'lumotlar to'plami ustida turli mashinaviy o'qitish algoritmlarining ishlash ko'rsatkichlari o'rganildi. Natijalar shuni ko'rsatadiki, XGBoost algoritmi 91.2% aniqlik ko'rsatkichi bilan eng yuqori natijaga erishdi, Logistic Regression esa 78.4% bilan sodda talqin etiluvchi alternativ sifatida tavsiya etildi. Ushbu tadqiqot ta'lim muassasalarida algoritmni tanlash bo'yicha amaliy ko'rsatmalar beradi.

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