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feature-extraction

Feature engineering is the process of creating, selecting, and transforming input features to improve the performance of machine learning models. It includes techniques such as feature extraction, feature selection, encoding categorical variables, scaling numerical features, and generating new features from existing data. Effective feature engineering helps models capture meaningful patterns, improve predictive accuracy, and generalize better to unseen data.

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It is my belief that you, the postgraduate students and job-seekers for whom the book is primarily meant will benefit from reading it; however, it is my hope that even the most experienced researchers will find it fascinating as well.

  • Updated Aug 22, 2025