Enhancing Machine Learning Models: A Deep Dive into Feature Engineering

The efficacy of machine learning models heavily depends on the quality of input data and features [1]. In traditional machine learning, transforming raw data into features is crucial for model accuracy. Feature engineering aims to transform existing data into informative, relevant, and discriminative features. Although deep learning and end-to-end learning have revolutionized and automated processing for images, text, and signals, feature engineering for relational and human behavioural data remains an iterative, slow and laborious task [2].

This article explores techniques …

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