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Question 41 - DP-100 discussion

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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
Answers
A.
Root Mean Square Error
B.
Coefficient of determination
Answers
B.
Coefficient of determination
C.
Recall
Answers
C.
Recall
D.
Precision
Answers
D.
Precision
E.
Mean absolute error
Answers
E.
Mean absolute error
Suggested answer: B

Explanation:

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.

Incorrect Answers:

A: Root mean squared error (RMSE) creates a single value that summarizes the error in the model. By squaring the difference, the metric disregards the difference between over-prediction and under-prediction.

C: Recall is the fraction of all correct results returned by the model.

D: Precision is the proportion of true results over all positive results.

E: Mean absolute error (MAE) measures how close the predictions are to the actual outcomes; thus, a lower score is better.

Reference:

https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/evaluate-model

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asked 02/10/2024
JULIUS BALNEG
37 questions
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