Scenario: You have 100M labeled examples in your gcp video
data-engineer-pro video for scenario: You have 100M labeled examples in your dataset while working on a regression problem in the natural language processing
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Scenario: You have 100M labeled examples in your dataset while working on a regression problem in the natural language processing domain. The data has been randomly shuffled and split into train and test samples in a 90/10 ratio. After training the neural network and evaluating the model on the test set, you notice that the root-mean-squared error (RMSE) of the model is twice as high on the train set as on the test set. Question: How can you enhance the model's performance in this situation?