Question

A data scientist is training a deep feed-forward neural network on a tabular dataset of 50,000 records to predict customer churn. During training, the model reaches 98% accuracy on the training set but only 74% accuracy on the validation set, and the gap widens as training continues. The team wants to improve generalization without collecting more data. Which change is MOST likely to reduce this generalization gap?

Accepted Answer

Add dropout layers and apply L2 weight regularization to the network

Answer

Increase the number of hidden layers and neurons to give the model more capacity

Answer

Raise the learning rate so the optimizer converges to the training minimum faster

Answer

Train for more epochs until the training accuracy reaches 100%