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Exam AWS Certified Machine Learning - Specialty topic 1 question 84 discussion

Exam question from Amazon's AWS Certified Machine Learning - Specialty
Question #: 84
Topic #: 1
[All AWS Certified Machine Learning - Specialty Questions]

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?

  • 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
Show Suggested Answer Hide Answer
Suggested Answer: B 🗳️
by Paul_NoName at Feb. 3, 2021, 7:56 p.m.
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Paul_NoName
Highly Voted 2 years, 6 months ago
B is correct answer .
upvoted 22 times
hamimelon
1 year, 4 months ago
Agree, B.
upvoted 2 times
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pravv
2 years, 6 months ago
why B is the correct answer and not C?
upvoted 1 times
SophieSu
2 years, 6 months ago
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.
upvoted 7 times
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hamimelon
1 year, 4 months ago
For example. If you have two variables, X and Y, and you have two data points. You want to solve the problem: aX1+bY1 = Z1, aX2 + bY2 = Z2. However, if Y=2X -> Y1 = 2X1, Y2 = 2X2, then problem becomes: aX1+bY1 = Z1, a*2X1 + b*2Y1 = Z2 = 2*Z1. So you end up with only one function: aX1+bY1=Z1, meaning there will be more than one answer for (a, b). If you are familiar with linear algebra, it's easier to express the concept.
upvoted 8 times
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Sneep
Highly Voted 1 year, 3 months ago
B: If two features in the dataset are perfectly linearly dependent, it means that one feature can be expressed as a linear combination of the other. This can create a singular matrix during optimization, as the linear model would be trying to fit a linear equation to a dataset where one variable is fully determined by the other. This would lead to an ill-defined optimization problem, as there would be no unique solution that minimizes the sum of the squares of the residuals. This could lead to problems during training, as the model would not be able to find appropriate parameter values to fit the data.
upvoted 7 times
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Mickey321
Most Recent 8 months ago
Selected Answer: B
Option B
upvoted 1 times
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AjoseO
1 year, 2 months ago
Selected Answer: B
The presence of linearly dependent features means that they are redundant, and provide no additional information to the model. This can result in a matrix that is not invertible, which is a requirement for solving a linear least squares regression problem. The presence of a singular matrix can also cause numerical instability and make it impossible to find an optimal solution to the optimization problem.
upvoted 4 times
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yemauricio
1 year, 4 months ago
Selected Answer: B
linera dependence creates singular matrix that causes problems at the moment we fit the modle
upvoted 4 times
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wisoxe8356
1 year, 4 months ago
Selected Answer: B
https://towardsdatascience.com/multi-collinearity-in-regression-fe7a2c1467ea B - two features are perfectly linearly dependent = singular matrix during optimization Not D - Not100% correct (as Multicollinearity happens when independent variables in the regression model are highly correlated to each other) they can still be independent variables
upvoted 3 times
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ovokpus
1 year, 10 months ago
Selected Answer: D
Consider one of the 5 assumptions of linear regression. This situation violates the assumption of "No multicollinearity between feature variables" Hence, D
upvoted 3 times
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jerto97
2 years, 5 months ago
B. See the multicollinearity problem in wikipedia https://en.wikipedia.org/wiki/Multicollinearity (second paragraph)
upvoted 4 times
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takahirokoyama
2 years, 6 months ago
This issue is overfitting.
upvoted 1 times
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