MASHINAVIY O‘QITISHDA XUSUSIYATLAR TANLOVI VA O‘ZGARTIRISHNING AHAMIYATI
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Abstrak:
Mashinaviy o‘qitish (machine learning) loyihalarida ma'lumotlarni tayyorlash bosqichi muvaffaqiyatning asosiy omillaridan biridir. Ushbu bosqichda xususiyatlar tanlovi (feature selection) va xususiyatlar o‘zgartirish (feature engineering) modelning samaradorligi, aniqligi va umumlashtirish qobiliyatiga katta ta'sir ko‘rsatadi. Ushbu maqolada ushbu jarayonlarning ahamiyati, afzalliklari va amaliy misollar keltiriladi.
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Guyon, I., & Elisseeff, A. (2003). An introduction to variable and feature selection. Journal of Machine Learning Research, 3, 1157-1182.
Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd ed.). Springer.
Kuhn, M., & Johnson, K. (2013). Applied Predictive Modeling. Springer.
Zheng, A., & Casari, A. (2018). Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists. O’Reilly Media.
Brownlee, J. (2020). Data Preparation for Machine Learning: Data Cleaning, Feature Selection, and Data Transforms in Python. Machine Learning Mastery.
Liu, H., & Motoda, H. (2007). Computational Methods of Feature Selection. Chapman and Hall/CRC.
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
Onlayn resurs: Scikit-learn Documentation. Feature Selection (https://scikit-learn.org/stable/modules/feature_selection.html).
Onlayn resurs: Kaggle Tutorials. Feature Engineering (https://www.kaggle.com/learn/feature-engineering).
Hall, M. A. (1999). Correlation-based Feature Selection for Machine Learning. Doctoral dissertation, University of Waikato.