MLS-C01 · Question #337
A machine learning engineer is building a bird classification model. The engineer randomly separates a dataset into a training dataset and a validation dataset. During the training phase, the model…
The correct answer is A. Perform stratified sampling on the original dataset. Stratified sampling ensures that each class is proportionally represented and mitigates the impact of class imbalance on model performance.
Question
A machine learning engineer is building a bird classification model. The engineer randomly separates a dataset into a training dataset and a validation dataset. During the training phase, the model achieves very high accuracy. However, the model did not generalize well during validation of the validation dataset. The engineer realizes that the original dataset was imbalanced. What should the engineer do to improve the validation accuracy of the model?
Options
- APerform stratified sampling on the original dataset.
- BAcquire additional data about the majority classes in the original dataset.
- CUse a smaller, randomly sampled version of the training dataset.
- DPerform systematic sampling on the original dataset.
How the community answered
(66 responses)- A71% (47)
- B6% (4)
- C5% (3)
- D18% (12)
Explanation
Stratified sampling ensures that each class is proportionally represented and mitigates the impact of class imbalance on model performance.
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