What is feature engineering? Why do I need feature engineering?

Feature engineering is the process of selecting, transforming, and extracting features (also known as variables or attributes) from raw data to build machine learning models. The quality and relevance of features can have a significant impact on the performance and accuracy of a model.

The goal of feature engineering is to create a set of features that are informative, relevant, and meaningful for a specific task. This involves domain knowledge, creativity, and statistical techniques to identify, transform, and combine features. For example, in a text classification task, features could be the frequency of specific words or the presence of certain patterns. In an image classification task, features could be the color or texture of specific regions of an image.

Feature engineering is an iterative process and requires expertise in the domain, as well as knowledge of the machine learning algorithms being used. A well-designed feature engineering process can help reduce overfitting, improve model performance, and increase interpretability.

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