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PROFESSIONAL-DATA-ENGINEER · Question #211
You work on a regression problem in a natural language processing domain, and you have 100M labeled exmaples in your dataset. You have randomly shuffled your data and split your dataset into train and
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Submitted by layla.eg· Mar 30, 2026Operationalizing machine learning models
Question
You work on a regression problem in a natural language processing domain, and you have 100M labeled exmaples in your dataset. You have randomly shuffled your data and split your dataset into train and test samples (in a 90/10 ratio). After you trained the neural network and evaluated your model on a test set, you discover that the root-mean-squared error (RMSE) of your model is twice as high on the train set as on the test set. How should you improve the performance of your model?
Options
- AIncrease the share of the test sample in the train-test split.
- BTry to collect more data and increase the size of your dataset.
- CTry out regularization techniques (e.g., dropout of batch normalization) to avoid overfitting.
- DIncrease the complexity of your model by, e.g., introducing an additional layer or increase sizing the size of vocabularies or n-grams used.
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Topics
#model underfitting#RMSE#model complexity#NLP regression