Definition: The process of selecting, transforming, and creating relevant features from raw data to improve the performance of machine learning models.
Better definition: When you help your computer pick out the most fashionable features from a data runway.
Where does this fit in the AI Landscape?
Feature engineering is a critical step in the AI development process, as it helps improve model performance and reduce training time. It's widely practiced across industries, and well-crafted features can often make the difference between a mediocre model and a highly effective one.
What are the real world impacts of this?
Feature Engineering is fundamental to the performance of AI systems. Good features can make our interactions with AI technologies more accurate and efficient, from personalized ad targeting to voice recognition. For developers, feature engineering is a critical skill, enabling the creation of more effective machine learning models by transforming and enriching input data.
What could go wrong in the real world with this?
An AI model with exceptional feature engineering skills is tasked with organizing a party but ends up overanalyzing the guest list and creating elaborate seating charts based on obscure connections between guests.
The process of creating new features from raw data to improve model performance. In this context, it could involve extracting specific patterns or structures from code or text.