Google Professional Machine Learning Engineer
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You are a machine learning engineer tasked with investigating failures of a production line component based on sensor readings. Your dataset contains sensor readings where less than 1% of the readings are positive examples representing failure incidents. You have attempted to train several classification models on this imbalanced dataset, but none of them converge. How should you address the class imbalance problem to improve model performance?
You are a machine learning engineer tasked with investigating failures of a production line component based on sensor readings. Your dataset contains sensor readings where less than 1% of the readings are positive examples representing failure incidents. You have attempted to train several classification models on this imbalanced dataset, but none of them converge. How should you address the class imbalance problem to improve model performance?
Exam-Like
A
Use the class distribution to generate 10% positive examples.
10.4%
B
Use a convolutional neural network with max pooling and softmax activation.
0.0%
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