Feature Engineering & Selection for Explainable Models: A Second Course for Data Scientists (Revised Edition)
A comprehensive resource on feature engineering and selection techniques for building transparent, interpretable machine learning models that balance performance with explainability.
About This Book
This book provides a focused exploration of feature engineering and selection methods essential for creating transparent and interpretable predictive models.
Readers will learn systematic approaches to transforming raw data into meaningful features while maintaining model explainability throughout the process.
The content emphasizes practical techniques that data scientists can apply to improve both model performance and stakeholder understanding of predictions.
Topics include feature creation strategies, selection algorithms, and evaluation frameworks designed specifically for explainable artificial intelligence applications.
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