Feature Engineering
What is Feature Engineering?
Feature engineering is a crucial step in the artificial intelligence and machine learning pipeline. It involves transforming raw data into meaningful features that can be used to train predictive models. This process often requires a deep understanding of the data and the problem at hand. By creating new features or modifying existing ones, data scientists can enhance the model's ability to learn and make accurate predictions. Feature engineering includes techniques like normalization, encoding categorical variables, creating interaction terms, and more. It is both an art and a science, requiring creativity and technical skills. Effective feature engineering can significantly impact the success of machine learning projects, making it a highly valued skill in the AI industry.
The process of using domain knowledge to extract features from raw data to improve the performance of machine learning models.
Examples
- A financial institution uses feature engineering to predict credit risk. They create new features like 'average balance over the last six months' and 'number of missed payments in the last year' to improve the predictive power of their model.
- In a health care application, engineers transform raw patient data into features such as 'age at diagnosis,' 'BMI,' and 'family history of illness.' These features help in building a model that can predict the likelihood of developing certain diseases.
Additional Information
- Feature engineering often involves domain experts who understand the intricacies of the data and the business problem.
- Automated machine learning (AutoML) tools are increasingly incorporating feature engineering capabilities to streamline the process.