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Exam Professional Machine Learning Engineer topic 1 question 60 discussion

Actual exam question from Google's Professional Machine Learning Engineer
Question #: 60
Topic #: 1
[All Professional Machine Learning Engineer Questions]

You work for a bank and are building a random forest model for fraud detection. You have a dataset that includes transactions, of which 1% are identified as fraudulent. Which data transformation strategy would likely improve the performance of your classifier?

  • A. Write your data in TFRecords.
  • B. Z-normalize all the numeric features.
  • C. Oversample the fraudulent transaction 10 times.
  • D. Use one-hot encoding on all categorical features.
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Suggested Answer: C 🗳️
by ralf_cc at July 10, 2021, 6:24 a.m.

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ralf_cc
Highly Voted 3 years, 11 months ago
C - https://swarit.medium.com/detecting-fraudulent-consumer-transactions-through-machine-learning-25b1f2cabbb4
upvoted 14 times
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NamitSehgal
Highly Voted 3 years, 5 months ago
Selected Answer: C
C is the answer
upvoted 5 times
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dija123
Most Recent 1 year ago
Selected Answer: C
Agree with C
upvoted 1 times
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PhilipKoku
1 year ago
Selected Answer: C
C) Oversample
upvoted 1 times
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MultiCloudIronMan
1 year, 2 months ago
Selected Answer: C
Oversampling increases the number of fraudulent transaction in the training data to enable the machine to learn how to predict them
upvoted 3 times
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fragkris
1 year, 6 months ago
Selected Answer: C
C - Even though most similar questions propose to downsample the majority (not fraudulent) and add weights to it.
upvoted 1 times
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M25
2 years, 1 month ago
Selected Answer: C
Went with C
upvoted 2 times
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wish0035
2 years, 6 months ago
Selected Answer: C
ans: C A, B, D => wouldnt help with imbalance
upvoted 1 times
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hiromi
2 years, 6 months ago
Selected Answer: C
C https://medium.com/analytics-vidhya/credit-card-fraud-detection-how-to-handle-imbalanced-dataset-1f18b6f881
upvoted 1 times
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Mohamed_Mossad
2 years, 11 months ago
Selected Answer: C
the best option is C
upvoted 1 times
...
Community vote distribution
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C (25%)
B (20%)
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