An exercise analytics company wants to predict running speeds for its customers by using a dataset that contains multiple health-related features for each customer. Some of the features originate from sensors that provide extremely noisy values.
The company is training a regression model by using the built-in Amazon SageMaker linear learner algorithm to predict the running speeds. While the company is training the model, a data scientist observes that the training loss decreases to almost zero, but validation loss increases.
Which technique should the data scientist use to optimally fit the model?

A. Add L1 regularization to the linear learner regression model.

B. Perform a principal component analysis (PCA) on the dataset. Use the linear learner regression model.

C. Perform feature engineering by including quadratic and cubic terms. Train the linear learner regression model.

D. Add L2 regularization to the linear learner regression model.








 

Suggested Answer: C

Community Answer: A




This question is in MLS-C01 AWS Certified Machine Learning – Specialty Exam
For getting AWS Certified Machine Learning – Specialty Certificate


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