What is meant by "model interpretability"?

Model interpretability refers to the degree to which the outputs of a model can be understood by humans. In data science, interpretability is crucial because it allows stakeholders to comprehend how a model arrives at its predictions, which can foster trust and facilitate better decision-making.

When a model is interpretable, it means that users can follow the logic behind the predictions, understand the significance of the input features, and gauge the model's reliability. This understanding is particularly important in fields such as healthcare, finance, and criminal justice, where model decisions can have substantial real-world consequences.

The other options address aspects that do not define model interpretability directly. For instance, the complexity of the model's structure relates to how intricate or convoluted a model is, which may actually hinder interpretability rather than enhance it. Predicting future outcomes is a fundamental purpose of modeling but does not inherently pertain to interpretability. Lastly, the speed of the model's execution focuses on performance and efficiency rather than how easily a model's workings and decisions can be comprehended by users.