MLA-C01 · Question #166
An ML engineer is developing a linear regression ML model. The model shows high accuracy on the training dataset but performs poorly on unseen new data. Which action should the ML engineer take to…
The correct answer is B. Apply ML techniques such as cross-validation and regularization. Use Amazon SageMaker. Applying cross-validation and regularization helps reduce overfitting by improving the model’s ability to generalize to unseen data. Tracking experiments allows systematic comparison of different regularization strengths and configurations to identify the version that performs…
Question
An ML engineer is developing a linear regression ML model. The model shows high accuracy on the training dataset but performs poorly on unseen new data. Which action should the ML engineer take to address this issue?
Options
- AIncrease the complexity of the model to capture more patterns in the training data. Use Amazon
- BApply ML techniques such as cross-validation and regularization. Use Amazon SageMaker
- CDirectly deploy the model into production. Use Amazon SageMaker Clarify to interpret model
- DIncrease the size of the training dataset without adjusting the size of the model. Retrain the model
How the community answered
(35 responses)- A11% (4)
- B77% (27)
- C9% (3)
- D3% (1)
Explanation
Applying cross-validation and regularization helps reduce overfitting by improving the model’s ability to generalize to unseen data. Tracking experiments allows systematic comparison of different regularization strengths and configurations to identify the version that performs best on
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