How should you resolve the class imbalance problem?

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?
A . Use the class distribution to generate 10% positive examples
B . Use a convolutional neural network with max pooling and softmax activation
C . Downsample the data with upweighting to create a sample with 10% positive examples
D . Remove negative examples until the numbers of positive and negative examples are equal

View Answer

Answer: C

Explanation:

https://developers.google.com/machine-learning/data-prep/construct/sampling-splitting/imbalanced-data#downsampling-and-upweighting

https://developers.google.com/machine-learning/data-prep/construct/sampling-splitting/imbalanced-data