In the context of model accuracy, when does a model become less effective?

A model becomes less effective when the behavior of the domain changes because it is designed to interpret relationships based on the data it was trained on. If the underlying patterns or trends in the data shift over time—due to market fluctuations, new regulations, changes in consumer behavior, or other factors—the predictive capabilities of the model can diminish. This phenomenon, known as concept drift, highlights that a model might perform well with historical data but can fail to make accurate predictions if the domain's characteristics evolve.

While complex features might enhance a model's ability to capture nuances in data, they do not inherently cause a decrease in effectiveness. Insufficient data can lead to unreliable predictions but does not necessarily render the model ineffective across all scenarios. The release of new algorithms could provide better options but does not directly affect the performance of existing models within their given context. Thus, continuing to adapt models to reflect changes in the domain is essential for maintaining their efficacy.