You trained an image classification model and see signs of overfitting: 99% training accuracy but only 75% test accuracy. What change should you make and why?

The correct answer is to increase the dropout rate at the flatten layer because the model is overfitting and lacks generalization. Overfitting occurs when a model learns the training data too well, including noise, leading to poor performance on unseen data. Dropout is a regularization technique that randomly sets a fraction of neurons to zero during training, preventing complex co-adaptations and forcing the network to learn more robust features, thus improving generalization. Increasing the learning rate is incorrect; a high learning rate can cause instability or divergence, and it's not a direct solution for overfitting. Increasing the size of the dense layer is incorrect because a more complex model (larger layer) would exacerbate overfitting, not solve it. Increasing the number of epochs is incorrect; training for more epochs would likely worsen overfitting, as the model would continue to memorize the training data.