Feature Vector

A feature vector converts the characteristics of an observation into a numerical format that a machine learning model can process. Each value in the vector corresponds to a specific feature, such as age, price, temperature, or an encoded category. Together, these values provide the model with the information it uses to identify patterns and make predictions.

Machine learning algorithms typically require data to be represented numerically. Feature vectors provide a consistent structure for representing individual observations, allowing models to compare data points, recognize relationships, and learn patterns during training.