Question 36 of 280
Single answerYou are building a model to predict daily temperatures. You split the data randomly and then transformed the
training and test datasets. Temperature data for model training is uploaded hourly. During testing, your model
performed with 97% accuracy; however, after deploying to production, the model's accuracy dropped to 66%. How
can you make your production model more accurate?
ANormalize the data for the training, and test datasets as two separate steps.
✓BSplit the training and test data based on time rather than a random split to avoid leakage.
CAdd more data to your test set to ensure that you have a fair distribution and sample for testing.
DApply data transformations before splitting, and cross-validate to make sure that the transformations are
applied to both the training and test sets
✓
Correct Answer: B
Split the training and test data based on time rather than a random split to avoid leakage.
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Explanation
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