What does the term "feature engineering" refer to in machine learning?

The term "feature engineering" refers to the process of selecting, modifying, or creating features to improve model performance. In machine learning, features are the individual measurable properties or characteristics of the data being used. The effectiveness of a model is often highly dependent on the quality and relevance of its features. By carefully engineering features, you can enhance the model’s ability to learn patterns and make accurate predictions.

For example, this can include transforming variables into a format that is more useful for analysis, combining multiple features into a single one, or creating new features based on domain knowledge. The goal of feature engineering is to provide the model with the most informative inputs possible, ultimately leading to better performance in tasks such as classification or regression.

While model performance evaluation, data cleaning, and working with large datasets are all important aspects of machine learning, they do not specifically encompass the concept of feature engineering, which is uniquely focused on the manipulation and optimization of the input variables used in model building.