RBF as Feature Engineering Explained Creating Nonlinear Features for ML
Автор: Learn AI with Rawal
Загружено: 2026-01-03
Просмотров: 3
In this video, we move beyond intuition and explain how the RBF (Radial Basis Function) kernel can be used as a feature engineering technique, not just as a kernel method.
You’ll learn how raw numeric features can be transformed into multiple similarity-based features using RBF, allowing even simple linear models to capture complex, nonlinear patterns. We also discuss how reference points are chosen, how gamma affects feature influence, and the trade-offs involved when creating multiple RBF features.
This video helps you understand why and how RBF features make models more expressive, instead of treating kernels as black-box magic.
#machinelearning #datascience #featureengineering #rbfkernel #mlpreprocessing #nonlinearfeatures #aiinprogress
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