What is a drawback to performing data cleansing (imputation, transformations, etc.) on raw data prior to partitioning the data for honest assessment as opposed to performing the data cleansing after partitioning the data?
Suggested Answer: D
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11 comments · newest first
TE
Terrilyn
12 months ago
Yeah, but it also takes more time to do it after, right?
LO
Louvenia
12 months ago
It omits the training and test data sets from the benefits of cleansing methods.
NA
Nan
1 year ago
Wait, I thought cleansing was always a good thing?
ZA
Zack
1 year ago
Totally agree, cleansing before partitioning can skew results.
JU
Junita
1 year ago
It violates assumptions of the model.
GL
Glory
1 year ago
I wonder if not being able to compare the effectiveness of different cleansing methods is a significant drawback, but I can't recall if that was emphasized in our studies.
FR
Frederica
1 year ago
I feel like the extra computational effort and time could be a concern, but it seems less relevant compared to the impact on the data sets.
LE
Leanna
1 year ago
I remember practicing a question about data partitioning, and I think it was mentioned that performing cleansing before partitioning could violate model assumptions.
DE
Destiny
1 year ago
I think one drawback is that it might omit the training and test data sets from the benefits of the cleansing methods, but I'm not entirely sure.
DU
Dulce
1 year ago
Okay, let me think this through. We need to restrict access based on the resource and the HTTP method. That means we need to look at both the resource and the action being performed. So I'm going to go with option C, resource and action.
IR
Irma
1 year ago
Okay, I've got an idea. Since the question is asking about what service consumers' work involves and how technology helps them, I'm guessing the answer is probably related to understanding the customer's perspective and needs. I'll go with Customer engagement.