Data Engineering
Feature Engineering
Creating new data columns or transforming existing ones to help an AI model learn better, the art of feeding AI the right inputs.
Definition
The process of using domain knowledge to create, select, and transform input variables (features) that improve model performance. Includes encoding, normalization, interaction terms, and temporal features.
Why it matters
Often has more impact on model performance than model selection, great features beat fancy algorithms.
Related terms in Data Engineering
Batch Processing
Processing a large group of data all at once on a schedule, rather than one piece at a time in real-time.
Chunking Strategies
Chopping up long documents into small, bite-sized pieces so an AI can search and read them easily.
Data Augmentation
Creating fake but realistic training examples (like flipping or rotating images) to give the AI more data to learn from.
Data Labeling
The human work of tagging data with correct answers so an AI can learn from it, like marking photos as "cat" or "dog."
From vocabulary to outcomes
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