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PROFESSIONAL-MACHINE-LEARNING-ENGINEER · Question #34
You were asked to investigate failures of a production line component based on sensor readings. After receiving the dataset, you discover that less than 1% of the readings are positive examples…
The correct answer is C. Downsample the data with upweighting to create a sample with 10% positive examples. less than 1% of the readings are positive - none of them converge. Downsampling (in this context) means training on a disproportionately low subset of the majority https://developers.google.com/machine-learning/data-prep/construct/sampling…
Submitted by neha2k· Apr 18, 2026Data processing and feature engineering
Question
You were asked to investigate failures of a production line component based on sensor readings. After receiving the dataset, you discover that less than 1% of the readings are positive examples representing failure incidents. You have tried to train several classification models, but none of them converge. How should you resolve the class imbalance problem?
Options
- AUse the class distribution to generate 10% positive examples.
- BUse a convolutional neural network with max pooling and softmax activation.
- CDownsample the data with upweighting to create a sample with 10% positive examples.
- DRemove negative examples until the numbers of positive and negative examples are equal.
How the community answered
(43 responses)- A19% (8)
- B9% (4)
- C67% (29)
- D5% (2)
Explanation
- less than 1% of the readings are positive - none of them converge. Downsampling (in this context) means training on a disproportionately low subset of the majority https://developers.google.com/machine-learning/data-prep/construct/sampling- splitting/imbalanced-data#downsampling-and-upweighting
Topics
#Class Imbalance#Data Resampling#Class Weighting#Data Preprocessing
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