AWS Certified Machine Learning – Specialty — Question 88

A Machine Learning Specialist is applying a linear least squares regression model to a dataset with 1,000 records and 50 features. Prior to training, the ML
Specialist notices that two features are perfectly linearly dependent.
Why could this be an issue for the linear least squares regression model?

Answer options

• A. It could cause the backpropagation algorithm to fail during training
• B. It could create a singular matrix during optimization, which fails to define a unique solution
• C. It could modify the loss function during optimization, causing it to fail during training
• D. It could introduce non-linear dependencies within the data, which could invalidate the linear assumptions of the model

Correct answer: B

Explanation

The correct answer is B because perfect linear dependence between features leads to a singular matrix, which means the linear regression cannot find a unique solution. Options A, C, and D are incorrect because they do not accurately describe the mathematical implications of multicollinearity in linear regression, which specifically relates to singular matrices and uniqueness of solutions.