Which of the following is a technique for selecting a subset of features in a model?

The correct choice is feature selection, which is specifically aimed at identifying and selecting a subset of relevant features (variables) for use in model construction. This technique is crucial when dealing with high-dimensional data, as it helps improve model performance, reduces overfitting, and makes models easier to interpret. By focusing on the most informative features, it can enhance the efficiency of the learning algorithm and reduce computational costs.

Feature engineering involves creating new features from the existing data to better capture the underlying patterns, rather than selecting existing features. Feature extraction is a process that transforms data into a lower-dimensional space, generating new features that represent combinations of the original features. Finally, feature scaling normalizes the range of independent variables or features of the data, ensuring that they contribute equally to the computation. None of these options focus primarily on the selection aspect, making feature selection the most appropriate choice.