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Exam DP-100 topic 5 question 13 discussion

Actual exam question from Microsoft's DP-100
Question #: 13
Topic #: 5
[All DP-100 Questions]

You are a data scientist creating a linear regression model.
You need to determine how closely the data fits the regression line.
Which metric should you review?

  • A. Root Mean Square Error
  • B. Coefficient of determination
  • C. Recall
  • D. Precision
  • E. Mean absolute error
Show Suggested Answer Hide Answer
Suggested Answer: B 🗳️

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BilJon
Highly Voted 4 years, 2 months ago
Shouldn't be E? Mean absolute error (MAE) measures how close the predictions are to the actual outcomes; thus, a lower score is better.
upvoted 10 times
pancman
3 years, 1 month ago
MAE is irrelevant to the question. You are being asked which metric measures how closely data fits the regression line. The given answer of R2 is correct.
upvoted 3 times
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tomiskolc
4 years, 1 month ago
but you won't know what is 'lower'. i mean you will get a number MAE = 250, it can be a good fit for example R2 = 0,95. If you get an other dataset you get MAE = 5 , but it stuill can be bad fit, it can be R2 = 0,2 . So you cant say about the fit based on only MAE (or RMSE) , but R2 can explain how good is the fit.
upvoted 5 times
bakemi5105
4 years ago
it was not asked wether the metric is in a specific range. So its not an argument to the exam question.
upvoted 1 times
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ljljljlj
Highly Voted 3 years, 11 months ago
On exam 2021/7/10
upvoted 7 times
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evangelist
Most Recent 11 months, 2 weeks ago
Selected Answer: B
The coefficient of determination (R-squared) is the most appropriate metric to determine how closely the data fits the regression line. It represents the proportion of the variance in the dependent variable that is predictable from the independent variable(s).
upvoted 1 times
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bbe8966
11 months, 3 weeks ago
correct!
upvoted 1 times
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evangelist
1 year ago
Selected Answer: B
correct
upvoted 1 times
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michaelmorar
2 years, 5 months ago
Selected Answer: B
CoD or R-squared
upvoted 2 times
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ning
2 years, 12 months ago
This question is poorly written, no definition of 'fits', I guess normally R2 is think of how fit it is ... but really you need define what fit is in the particular situation, otherwise, A / E could be candidates as well ...
upvoted 2 times
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pancman
3 years, 1 month ago
Selected Answer: B
Given answer is correct. Coefficient of determination, often referred to as R2 measures how closely data fits the regression line.
upvoted 4 times
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Shanggavee
3 years, 7 months ago
correct answer is B
upvoted 3 times
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dijaa
3 years, 9 months ago
R-squared is a statistical measure of how close the data are to the fitted regression line. It is also known as the coefficient of determination,
upvoted 5 times
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pwell
3 years, 11 months ago
The answer B is correct R^2 measures how closely data fits a regression line
upvoted 4 times
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bakemi5105
4 years ago
Would say E - MAE directly is using the difference between the prediction and the true value. RMSE and R2 are both using squared distance for the residual
upvoted 1 times
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ali25
4 years, 2 months ago
A model is considered to "fit" the data well if the difference between observed and predicted values is small. Coefficient of determination, often referred to as R2, represents the predictive power of the model as a value between 0 and 1. Zero means the model is random (explains nothing); 1 means there is a perfect "fit". However, caution should be used in interpreting R2 values, as low values can be entirely normal and high values can be suspect.
upvoted 1 times
ali25
4 years, 2 months ago
The most common interpretation of the coefficient of determination is how well the regression model fits the observed data. For example, a coefficient of determination of 60% shows that 60% of the data fit the regression model. Generally, a higher coefficient indicates a better fit for the model.
upvoted 1 times
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