Which phase in ML project is explicitly responsible for selecting the algorithm to use?

• A. Model selection
• B. Data collection and preprocessing
• C. Model training
• D. Definition of problem or task

Answer: (A)

Deciding which algorithm to use is done in the model selection phase. This stage happens after you’ve defined the task and prepared your data, and it focuses on choosing the most suitable algorithm family for the problem. You compare a few candidate algorithms (for example, linear models, tree-based methods, or neural networks) based on the data characteristics, the problem type (classification, regression, etc.), and practical considerations like expected training time and interpretability. Through evaluation methods such as cross-validation and relevant metrics on validation data, you settle on the approach that offers the best balance of performance and practicality. Once the algorithm is chosen, you move on to training and hyperparameter tuning. Data collection and preprocessing are about getting clean, usable data; defining the problem sets the goal and requirements; training fits the selected model to the data.