Feature engineering is the process of transforming raw data into features that are suitable for machine learning models. It involves selecting, manipulating, and transforming raw data into features that can be used in supervised learning. The goal of feature engineering is to create a set of informative and relevant features that can be used to improve the performance of a machine learning model. Feature engineering is a crucial step in machine learning, as the success of machine learning models heavily depends on the quality of the features used to train them.
Feature engineering consists of mainly five processes: feature creation, feature transformation, feature extraction, feature selection, and feature scaling. These processes are iterative and require experimentation and testing to find the best combination of features for a given problem. Automated feature engineering has been available in some machine learning software since 2016.
While deep learning networks, such as convolutional neural networks, can learn features by themselves, feature engineering is still important for improving the accuracy of machine learning models. It can produce new features for both supervised and unsupervised learning, with the goal of simplifying and speeding up data transformations while also enhancing model accuracy. However, feature engineering can be a time-consuming and error-prone process, as it requires domain expertise and often involves trial and error.
