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Databricks Machine Learning Professional Exam - Topic 12 Question 31 Discussion

Actual exam question for Databricks's Databricks Machine Learning Professional exam
Question #: 31
Topic #: 12
[All Databricks Machine Learning Professional Questions]

Which of the following describes label drift?

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Suggested Answer: C

by Kaitlyn at Nov 13, 2024, 10:32 AM

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Jerry
3 months ago
None of these options seem to nail it down completely.
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Casie
3 months ago
I agree, it's all about the target variable changing.
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Melissa
3 months ago
Wait, I thought label drift was just a myth?
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Brock
4 months ago
I think it's more about input variables, not targets.
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Blossom
4 months ago
Label drift is definitely about changes in target distribution.
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Louis
4 months ago
From what I studied, label drift is definitely about changes in the target variable's distribution. I think option E might be the right answer.
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Miriam
4 months ago
I'm a bit confused. I feel like label drift could be about input variables too, but I can't recall the exact definition.
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Elliott
4 months ago
I remember practicing a question about label drift, and I think it was related to how the model's predictions change over time.
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Francine
5 months ago
I think label drift has something to do with changes in the target variable, but I'm not sure if it's just the distribution or something else.
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Fletcher
5 months ago
I'm not sure about this one. The options seem quite similar, and I'm not sure I fully understand the concept of label drift. I'll have to guess and hope for the best.
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Sheridan
5 months ago
I'm pretty confident that the answer is E - label drift is when there is a change in the distribution of a target variable. That's what I remember from the lectures.
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Wade
5 months ago
Okay, I've got this. Label drift is about changes in the relationship between the input variables and the target variable, so I'm going to go with option D.
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Bulah
5 months ago
Hmm, I'm a bit confused by this question. I'm not entirely sure what "label drift" means. I'll have to think about it carefully and review my notes before selecting an answer.
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Gail
5 months ago
This question seems straightforward. I think the correct answer is A - label drift is when there is a change in the distribution of the predicted target given by the model.
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Ryan
1 year ago
Label drift, huh? Sounds like a new dance move. I'll stick with the Macarena for now.
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Ahmed
1 year ago
B? Really? That's a cop-out answer. Of course one of these options describes label drift!
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Erasmo
1 year ago
Hmm, I'm going with E. Changes in the target variable distribution is the essence of label drift, isn't it?
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Cory
1 year ago
No, label drift is specifically about changes in the distribution of the target variable.
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Inocencia
1 year ago
So, you're saying label drift is when there is a change in the relationship between input variables and target variables?
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Jaime
1 year ago
Actually, I believe label drift is when there is a change in the distribution of a target variable.
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Gearldine
1 year ago
I think label drift is when there is a change in the distribution of the predicted target given by the model.
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Telma
1 year ago
D sounds good to me. The relationship between inputs and targets is key for label drift.
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My
1 year ago
Yes, D is the most accurate description of label drift.
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Mozelle
1 year ago
I agree, D is the correct option for label drift.
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Stevie
1 year ago
I think option A is the correct answer. It seems to best describe what label drift is all about.
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Nathan
1 year ago
I think we should go with option A for the answer.
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Kaycee
1 year ago
I'm not sure, but I think option D could also be a possibility.
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Alaine
1 year ago
I think label drift is when there is a change in the distribution of the predicted target given by the model.
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Shaunna
1 year ago
I agree, option A is the correct answer.
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Kris
1 year ago
Hmm, I see your point. But I still think it's about the predicted target distribution changing.
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Johnetta
1 year ago
I disagree, I believe label drift is when there is a change in the distribution of a target variable.
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Kris
1 year ago
I think label drift is when there is a change in the distribution of the predicted target given by the model.
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