What issue is likely to arise in the early stages of using an adaptive model?

In the early stages of using an adaptive model, erratic or bad predictions are a common issue due to the model's initial reliance on a limited dataset. Adaptive models learn and evolve over time as they are exposed to more data and feedback, but in their infancy, they may not have enough relevant information to make accurate predictions. This can result in inconsistencies and inaccuracies in decision-making.

As the model continues to gather data and refine its algorithms, predictions are expected to improve. However, during the initial phase, the learning process is heavily influenced by the quality and quantity of the data it has been trained on, which often doesn't capture the full complexity of real-world scenarios. Thus, the potential for erratic or poor predictions is a significant concern as the model is still in development.