FEATURE ENGINEERING: MUHIM HUSUSIYATLARNI TANLASH VA YARATISH USULLARI
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https://doi.org/10.5281/zenodo.20354979;
Feature Engineering, xususiyatlarni tanlash, xususiyatlarni yaratish, overfitting, ma’lumotlar transformatsiyasi, korrelyatsiya, Lasso, mashina o‘rganish, Pandas, Scikit-learn.Abstrak
Maqolada mashina o‘rganish modellarining samaradorligini oshirishda Feature Engineering (xususiyatlar muhandisligi) jarayonining o‘rni va ahamiyati yoritilgan. Unda xususiyatlarni tanlashning statistik (filtrlash), iteratsion (o‘rash) va model ichiga o‘rnatilgan usullari batafsil tahlil qilingan. Shuningdek, yangi xususiyatlar yaratish strategiyalari, ma’lumotlarni transformatsiya qilish, domen bilimining ahamiyati hamda Python ekotizimidagi asosiy kutubxonalar (Pandas, Scikit-learn) ko‘rib chiqilgan. Xulosada sohaning avtomatlashtirish istiqbollari va mutaxassis intuitsiyasining o‘rni ta’kidlangan.Iqtiboslar
Bishop, C. M. Pattern Recognition and Machine Learning. Springer, 2006. – pp. 173–186.
Guyon, I., & Elisseeff, A. An Introduction to Variable and Feature Selection. Journal of Machine Learning Research, 2003. – pp. 1157–1182.
Pedregosa, F., et al. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 2011. – vol. 12, pp. 2825–2830.
Kuhn, M., & Johnson, K. Feature Engineering and Selection: A Practical Approach for Predictive Models. CRC Press, 2019. – pp. 45–78.
Chollet, F. Deep Learning with Python. Manning Publications, 2018. – pp. 101–115.
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2026-05-23
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Olimjonova, H., & Sobirjonov, B. (2026). FEATURE ENGINEERING: MUHIM HUSUSIYATLARNI TANLASH VA YARATISH USULLARI. Ilm-Fan Va Innovatsiya, 4(45), 55-59. https://doi.org/10.5281/zenodo.20354979
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