AUTOMATED MACHINE LEARNING YORDAMIDA AVTOMATIK MODEL TANLASH
Keywords:
Kalit so‘zlar: AutoML, model tanlash, MO, giperparametr sozlash, meta- o‘qitish, Bayesian optimallashtirish, NAS, xususiyatlar muhandisligi, model baholash, oldindan qayta ishlash, SI.Abstract
Annotatsiya: Ushbu maqola AutoML texnologiyalaridan foydalanib avtomatik
model tanlash jarayonini tahlil qiladi. An’anaviy mashinaviy o‘qitishda ko‘p vaqt
oladigan algoritm tanlash va giperparametrlarni sozlash AutoML yordamida
avtomatlashtiriladi. Tadqiqotda AutoML tizimlarining ishlash prinsiplari,
optimallashtirish usullari va platformalarning samaradorligi ko‘rib chiqildi. Natijalar
AutoML model ishlab chiqishni soddalashtirishi va yuqori aniqlikdagi modellarni tez
yaratishga imkon berishini ko‘rsatdi.
References
Foydalanilgan adabiyotlar
1. Hutter, F., Kotthoff, L., & Vanschoren, J. (2019). Automated Machine Learning:
Methods, Systems, Challenges. Springer.
2. Feurer, M., & Hutter, F. (2019). Hyperparameter Optimization. In Automated
Machine Learning (pp. 3–33). Springer.
3. Olson, R. S., & Moore, J. H. (2016). TPOT: A Tree-Based Pipeline Optimization
Tool for Automating Machine Learning. In Proceedings of the 2016 Python in
Science Conference.
4. He, X., Zhao, K., & Chu, X. (2021). AutoML: A Survey of the State-of-the-Art.
Knowledge-Based Systems.
5. Thornton, C., Hutter, F., Hoos, H. H., & Leyton-Brown, K. (2013). Auto-WEKA:
Combined Selection and Hyperparameter Optimization of Classification
Algorithms. In Proceedings of the 19th ACM SIGKDD International Conference on
Knowledge Discovery and Data Mining.