Feature engineering is useful to improve the performance of machine learning algorithms and is often considered as applied machine learning. ... Selecting the important features and reducing the size of the feature set makes computation in machine learning and data analytic algorithms more feasible.

What is feature engineering and why is it important?

Feature Engineering is a very important step in machine learning. Feature engineering refers to the process of designing artificial features into an algorithm. These artificial features are then used by that algorithm in order to improve its performance, or in other words reap better results.

What is the use of feature engineering?

Feature engineering is the process of transforming raw data into features that better represent the underlying problem to the predictive models, resulting in improved model accuracy on unseen data.