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Exam Professional Machine Learning Engineer All Questions

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Exam Professional Machine Learning Engineer topic 1 question 36 discussion

Actual exam question from Google's Professional Machine Learning Engineer
Question #: 36
Topic #: 1
[All Professional Machine Learning Engineer Questions]

You 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?

  • A. Normalize the data for the training, and test datasets as two separate steps.
  • B. Split the training and test data based on time rather than a random split to avoid leakage.
  • C. Add more data to your test set to ensure that you have a fair distribution and sample for testing.
  • D. Apply data transformations before splitting, and cross-validate to make sure that the transformations are applied to both the training and test sets.
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Suggested Answer: B 🗳️
by maartenalexander at June 22, 2021, 12:46 p.m.

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maartenalexander
Highly Voted 3 years, 10 months ago
B. If you do time series prediction, you can't borrow information from the future to predict the future. If you do, you are artificially increasing your accuracy.
upvoted 35 times
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desertlotus1211
Most Recent 3 months, 3 weeks ago
Selected Answer: B
D is incorrect: Applying transformations before splitting is important, but it does not resolve the issue of time leakage. Even if transformations are done correctly, the random split will still lead to inflated test accuracy and poor production performance. This option focuses on correct data processing, but it does not address the leakage caused by random splitting in time series data.
upvoted 1 times
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baimus
7 months, 1 week ago
Selected Answer: D
It's D
upvoted 1 times
baimus
7 months, 1 week ago
B I mean. Sorry I wrote that comment very early and there is no delete key!
upvoted 1 times
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jsalvasoler
8 months, 2 weeks ago
Selected Answer: B
temporal split is a must in time series forecasting evaluation
upvoted 1 times
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PhilipKoku
10 months, 2 weeks ago
Selected Answer: B
B) Time split to avoid leaking data.
upvoted 1 times
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fragkris
1 year, 4 months ago
Selected Answer: B
Definetely B
upvoted 1 times
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Sum_Sum
1 year, 5 months ago
Selected Answer: B
they did not explicitly say forecasting, but splitting by time is the number one rule you learn
upvoted 1 times
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M25
1 year, 11 months ago
Selected Answer: B
Went with B
upvoted 1 times
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SergioRubiano
2 years ago
Selected Answer: D
D is correct. cross-validate
upvoted 2 times
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Mohamed_Mossad
2 years, 10 months ago
Selected Answer: B
train accuracy 97% , production accuracy 66% ---> time series data ---> random split ---> cause leakage , answer is B
upvoted 2 times
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David_ml
2 years, 11 months ago
Selected Answer: B
You don't split data randomly for time series prediction.
upvoted 3 times
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mmona19
3 years ago
Selected Answer: B
B should be the answer. D is incorrect as normalize before split is going to do data leak https://community.rapidminer.com/discussion/32592/normalising-data-before-data-split-or-after
upvoted 2 times
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giaZ
3 years, 1 month ago
Selected Answer: B
If you do random split in a time series, your risk that training data will contain information about the target (definition of leakage), but similar data won't be available when the model is used for prediction. Leakage causes the model to look accurate until you start making actual predictions with it.
upvoted 3 times
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xiaoF
3 years, 2 months ago
agree B as well
upvoted 2 times
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JobQ
3 years, 3 months ago
I think is B
upvoted 2 times
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Danny2021
3 years, 7 months ago
B. D doesn't improve anything at all. Split and Transform is no different than Transform and Split if the transform logic is the same.
upvoted 3 times
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Jijiji
3 years, 7 months ago
seems like D
upvoted 1 times
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