In the data science workflow, building a model includes which key activities?

Building a model is a crucial step in the data science workflow that involves several key activities, among which selecting algorithms and evaluating the model is foundational. This process entails choosing the appropriate machine learning algorithms based on the specific problem type (such as classification or regression) and the nature of the data. It includes exploring various algorithms to identify which one optimally captures the underlying patterns in the data.

Evaluating the model is equally significant, as it involves using metrics to assess its performance against the desired objectives. This evaluation process allows data scientists to understand how well the model generalizes to unseen data and whether it meets the required accuracy or other performance benchmarks. Through techniques such as cross-validation, data scientists can ascertain the reliability and robustness of their chosen model.

While the other options touch on important aspects of the data science process, they are not directly part of the model building phase. Selecting output metrics, determining the dataset source, and identifying team roles, while all necessary steps in the overall workflow, do not directly contribute to the act of building and refining the model itself.