What is the process of refining an algorithm so that it can learn from a data set?

Training is the correct answer because it refers specifically to the process where the algorithm is exposed to a data set in order to learn patterns and relationships within the data. During training, the algorithm adjusts its parameters based on the input data and the expected output, which helps it to minimize error and improve its performance in making predictions.

Through this iterative process, the algorithm becomes better at recognizing underlying structures in the data, effectively enabling it to generate more accurate predictions when it encounters new, unseen data. This foundational step is central to most machine learning models, as it sets the stage for subsequent processes like scoring and prediction.

Scoring, on the other hand, involves evaluating how well the trained algorithm performs on a data set, usually by calculating metrics such as accuracy or precision. Prediction refers to the output generated by the trained algorithm when it applies what it has learned to new data points. While machine learning encompasses the entire field dedicated to developing and refining algorithms, the act of refining an algorithm specifically is best described through the training process.