Intermediate · Fundamentals
Featurization
Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.
TL;DR. Featurization is the process of transforming raw input data into a feature vector that a machine learning model can process.
Technical Definition
Featurization is the process of transforming raw input data into a feature vector that a machine learning model can process.
How it works
Featurization is the core process of feature engineering, where raw data from various sources like text, images, or sensor readings are converted into numerical features. These features are then organized into a feature vector, making them suitable for input into machine learning algorithms. It often involves techniques like one-hot encoding or TF-IDF.
Related Concepts
- Feature Engineering — The process of creating, selecting, and transforming input variables to improve a machine learning model's performance.
- Feature vector — A feature vector is a numerical representation of an example, composed of its feature values, used as input for machine learning models.