A machine learning (ML) specialist is developing a deep learning sentiment analysis model that is based on data from movie reviews. After the ML specialist trains the model and reviews the model…

Shuffling the dataset with a different seed primarily affects the order of data presentation during training, which might impact convergence but does not directly address the fundamental issue of a model being too complex and memorizing training data.

Decreasing the learning rate mainly influences the speed and stability of model convergence, not the inherent complexity or capacity of the model to overfit.

Increasing the number of layers in the network would make the model more complex, which is generally counterproductive for reducing overfitting and would likely exacerbate the problem.

L1 and L2 regularization add penalties to the loss function based on weight magnitudes, encouraging smaller weights and preventing the model from becoming overly complex, thereby improving generalization and reducing overfitting.

Dropout is an effective regularization technique that randomly deactivates neurons during training, forcing the network to learn more robust features and preventing co-adaptation of neurons, which helps to mitigate overfitting.

Overfitting often occurs when a model is too complex for the available data, so decreasing the number of layers in the network reduces the model's capacity and complexity, making it less prone to memorizing the training data.