In a machine learning context, what is a feature attribution?

Feature attribution refers to the process of determining which features (or input variables) in a dataset contributed the most to the predictions made by a machine learning model. This is essential, as it helps in understanding how and why a model arrives at its decisions, enhancing transparency and interpretability.

By assessing feature attribution, practitioners can identify significant features that influence outcomes, which can also lead to insights for improving model performance and gaining a deeper understanding of the underlying data patterns. This is particularly important in applications where explainability is crucial, such as in healthcare or finance, where stakeholders need to understand model predictions to make informed decisions.

The other options involve different aspects of machine learning. Cleaning data is a preprocessing step, while measuring model accuracy typically involves metrics like accuracy, precision, or recall, not feature attribution. Data augmentation refers to techniques for increasing the diversity of training data without collecting new data, often used in training models, especially in computer vision tasks. Each of these concepts plays a role in the machine learning process but does not define feature attribution.