Feature engineering: Difference between revisions

Feature engineering is essential in machine learning for several reasons. Firstly, it improves performance and [[accuracy]] of [[models]] by providing a more informative representation of data. Secondly, it reduces [[dimensionality]] by eliminating irrelevant or redundant features which simplifies the learning process and increases computational efficiency. Thirdly, feature engineering helps address issues like [[overfitting]] or [[underfitting]] by maintaining an appropriate balance between [[bias]] and [[variance]]. Finally, feature engineering improves the interpretability and explainability of machine learning models - essential qualities required in many real-world applications.