Exam AWS Certified Machine Learning - Specialty topic 1 question 234 discussion

Exam question from Amazon's AWS Certified Machine Learning - Specialty

Question #: 234
Topic #: 1

[All AWS Certified Machine Learning - Specialty Questions]

A credit card company wants to identify fraudulent transactions in real time. A data scientist builds a machine learning model for this purpose. The transactional data is captured and stored in Amazon S3. The historic data is already labeled with two classes: fraud (positive) and fair transactions (negative). The data scientist removes all the missing data and builds a classifier by using the XGBoost algorithm in Amazon SageMaker. The model produces the following results:

• True positive rate (TPR): 0.700
• False negative rate (FNR): 0.300
• True negative rate (TNR): 0.977
• False positive rate (FPR): 0.023
• Overall accuracy: 0.949

Which solution should the data scientist use to improve the performance of the model?

A. Apply the Synthetic Minority Oversampling Technique (SMOTE) on the minority class in the training dataset. Retrain the model with the updated training data.
B. Apply the Synthetic Minority Oversampling Technique (SMOTE) on the majority class in the training dataset. Retrain the model with the updated training data.
C. Undersample the minority class.
D. Oversample the majority class.

Show Suggested Answer

Suggested Answer: A 🗳️

by sevosevo at March 18, 2023, 2:14 p.m.

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Comments

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blanco750

Highly Voted 1 year, 3 months ago

Selected Answer: A

SMOTE for minority class for unbalanced data

upvoted 6 times

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loict

Most Recent 9 months, 3 weeks ago

Selected Answer: A

A. YES - we want to oversample the minority class = Fraud B. NO - we want more fraudulent cases C. NO - we want more fraudulent cases D. NO - we want more fraudulent cases

upvoted 3 times

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Mickey321

10 months, 1 week ago

Selected Answer: A

By applying SMOTE, you can balance the class distribution and increase the diversity of your data, which can help your model learn better and reduce overfitting1. You can use the imbalanced-learn library in Python to implement SMOTE on your data2.

upvoted 1 times

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sevosevo

1 year, 3 months ago

Selected Answer: A

https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&cad=rja&uact=8&ved=2ahUKEwjLkqfb1OX9AhXkQ0EAHYlVDq0QFnoECBMQAw&url=https%3A%2F%2Ftowardsdatascience.com%2F5-smote-techniques-for-oversampling-your-imbalance-data-b8155bdbe2b5&usg=AOvVaw1FdrxDEbLDjNhacXn3d-Tu

upvoted 2 times

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