MLS-C01 · Question #94
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…
The correct answer is B. It could create a singular matrix during optimization, which fails to define a unique solution. A square matrix is singular, that is, its determinant is zero, if it contains rows or columns which are proportionally interrelated; in other words, one or more of its rows (columns) is exactly expressible as a linear combination of all or some other its rows (columns), the…
Question
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?
Options
- AIt could cause the backpropagation algorithm to fail during training
- BIt could create a singular matrix during optimization, which fails to define a unique solution
- CIt could modify the loss function during optimization, causing it to fail during training
- DIt could introduce non-linear dependencies within the data, which could invalidate the linear
How the community answered
(17 responses)- B94% (16)
- D6% (1)
Explanation
A square matrix is singular, that is, its determinant is zero, if it contains rows or columns which are proportionally interrelated; in other words, one or more of its rows (columns) is exactly expressible as a linear combination of all or some other its rows (columns), the combination being without a constant term.
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