During the evaluation phase of an AI model, what could cause accuracy to initially improve and then plateau before declining?

The scenario described highlights the behavior of a model's accuracy during its evaluation phase. Initially, accuracy improves as the model learns from the data, but later it plateaus and eventually declines. A learning rate that is set too high can lead to instability during training. When the learning rate is excessive, the model may adopt values that oscillate wildly rather than converging smoothly towards an optimal solution. As a result, you may observe fluctuations in accuracy.

Initially, rapid adjustments can yield improvements in performance, but as the model continues training, those aggressive updates may cause the weights to stray from values that lead to generalization. This situation could explain the plateau as the model struggles to stabilize, and eventually, it may start to deteriorate in performance, represented by declining accuracy. This behavior aligns with the notion that an overly high learning rate may prevent the model from properly finding the optimal parameters.

The other options present different phenomena. Regularization, when applied appropriately, generally helps in preventing overfitting and typically does not cause such behavior. An inadequate dataset size may hinder the model’s ability to learn effectively from diverse examples but would not cause a plateau followed by a decline. Lastly, overfitting is characterized by enhanced performance on training data while performance on unseen