Exam Professional Machine Learning Engineer topic 1 question 153 discussion

Actual exam question from Google's Professional Machine Learning Engineer

Question #: 153
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. Modify the target variable using the Box-Cox transformation.
B. Z-normalize all the numeric features.
C. Oversample the fraudulent transaction 10 times.
D. Log transform all numeric features.

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Suggested Answer: C 🗳️

by TNT87 at Feb. 9, 2023, 12:48 p.m.

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Scipione_

Highly Voted 1 year, 11 months ago

Selected Answer: C

The answer is C beacause it's the only way to improve model performance. Box-Cox transformation: transform feature values according to normal distribution Z-normalization: transform feature values according to x_new = (x – μ) / σ (so {x_new} have mean 0 and std dev 1) Log transform: just log transformation Also, the Random Forest algorithm is not a distance-based model but it is a tree-based model, there's no need of normalization process.

upvoted 5 times

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fitri001

Most Recent 8 months, 3 weeks ago

Selected Answer: C

Oversampling is a common technique to address class imbalance and can significantly improve the performance of the random forest model in fraud detection. It's important to note that oversampling can lead to overfitting, so monitoring the model's performance on unseen data (validation set) is crucial. You might also consider exploring other techniques like undersampling the majority class or using SMOTE (Synthetic Minority Oversampling Technique) for a more balanced approach.

upvoted 3 times

fitri001

8 months, 3 weeks ago

Class Imbalance: The dataset has a significant class imbalance, with only 1% of transactions being fraudulent (minority class). Random forest models can be biased towards the majority class during training. Oversampling: Oversampling replicates instances from the minority class (fraudulent transactions) in this case. By increasing the representation of the fraudulent class (10 times in this scenario), the model is exposed to more examples of fraud, improving its ability to learn and detect fraudulent patterns.

upvoted 1 times

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