Feature Selection: From Foundations to Variational Frontiers: Designing Stable, Interpretable, and High-Dimensional Machine Learning Systems
by Omar Soub
Feature Selection: From Foundations to Variational Frontiers presents a focused treatment of feature selection for machine learning. It brings together foundational concepts and variational perspectives while centering the practical and theoretical goals of stability, interpretability, and reliable performance in high-dimensional systems.
About This Book
Feature selection is examined as a central challenge in designing effective machine learning systems.
The book connects foundational perspectives with variational frontiers in feature selection.
Its focus includes stability, interpretability, and the demands of high-dimensional data.
This work is suited to readers seeking a technical perspective on selecting meaningful features for machine learning.
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