A predictive model uses a data set that has several variables with missing values.
What two problems can arise with this model? (Choose two.)
Suggested Answer: C, D
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13 comments · newest first
SA
Sabine
12 months ago
High collinearity isn't really a problem caused by missing values either.
AL
Almeta
12 months ago
Overfitting isn't really a direct issue from missing values, though.
BR
Brynn
1 year ago
Wait, does that mean we can't score new cases with missing data?
CR
Cristy
1 year ago
I agree, complete case analysis really cuts down the data!
MA
Madelyn
1 year ago
Missing values can definitely lead to fewer observations in the model.
OL
Olive
1 year ago
Collinearity seems more related to the relationship between variables, but I guess missing values could still affect the model in other ways.
FR
Freida
1 year ago
I feel like overfitting is more about the model complexity rather than missing values, so I'm uncertain about option A.
GA
Garry
1 year ago
I remember something about how missing values can complicate scoring new cases, so option D might be correct too.
SC
Scarlet
1 year ago
I think one problem could be that complete case analysis leads to fewer observations, but I'm not sure if that's the only issue.
TI
Tiffiny
1 year ago
Easy peasy! I'd definitely use the Adobe Experience Cloud debugger to verify the data is on the live site. That's the quickest and most reliable way to confirm it.
ET
Ettie
1 year ago
Hmm, this seems like a tricky one. I'll need to carefully go through all the details and try to piece it together step-by-step.
RO
Roosevelt
1 year ago
I think I'm going to go with option A. Using the Azure DevOps release pipeline to manage the staging VMs seems like the most integrated approach, and it should minimize the manual work. I just need to make sure I set up the tasks correctly.
MA
Man
1 year ago
Hmm, I'm a bit unsure about this one. I'll need to think through the different approaches and how they might impact the project. Maybe I'll make some quick notes to help me decide.