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A training dataset Is being prepared for a Einstein Discovery story. One skewed with outliers. at action should the Einstein consultant take?
Suggested Answer: C
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36 comments · newest first
SE
Serina
11 months ago
Changing the binning method could help too, but not always necessary.
KA
Kami
11 months ago
Nothing wrong with keeping the field if it's not the outcome variable.
TA
Tamra
12 months ago
Wait, what if the outliers are actually important?
GL
Glendora
12 months ago
Agree, outliers can skew results a lot!
MI
Mira
1 year ago
I think removing outlier rows is usually a good idea.
BR
Brandon
1 year ago
Removing the field seems drastic, especially if it has some useful information. I feel like we should explore other options first.
AD
Adelina
1 year ago
Changing the binning method could help with skewness, but I can't recall if fixed width is the right choice here.
CO
Cora
1 year ago
I think if the field isn't the outcome variable, then maybe we don't need to worry about the outliers too much? That sounds familiar.
YU
Yuki
1 year ago
I remember we discussed how outliers can skew results, but I'm not sure if removing them is always the best option.
AL
Alaine
1 year ago
I'm pretty confident the right answer is A - remove the outlier rows. Leaving in skewed data with outliers could really throw off the model, so cleaning that up first is the way to go.
LA
Lauran
1 year ago
Okay, I think I've got this. Since the question is about preparing the dataset for an Einstein Discovery story, the outliers are likely in the input data, not the target variable. In that case, I'd go with option A and remove the outlier rows.
JE
Jeffrey
1 year ago
Hmm, I'm a bit confused. The question mentions the dataset is skewed with outliers, but it's not clear which field that applies to. I'll need to think through the implications of each answer choice.
CA
Carla
1 year ago
This question seems straightforward, but I want to make sure I understand the context. Is the skewed dataset the input data or the target variable?
CH
Christiane
1 year ago
This seems straightforward, but I want to be careful not to overlook anything. I'll methodically go through each of the listed settings and make sure they're all properly configured.
DO
Domingo
1 year ago
I studied something similar about configuring Cisco ISE, and I think option B might be relevant, but I might be mixing it up with something else.
TR
Trevor
1 year ago
I'm leaning towards A, but I want to double-check my understanding. The RADIUS server is responsible for authenticating users and devices, right? That's a key component of WPA2 Enterprise.
NA
Nan
2 years ago
Removing outliers? Pffft, that's like trying to tame a wild Einstein. I say we just let the data do its thing!
KR
Kristofer
2 years ago
B) Nothing, because the field is not the outcome variable.
AM
Amber
2 years ago
Removing outliers can help improve the accuracy of the model.
NO
Novella
2 years ago
A) Remove the outlier rows.
NO
Norah
2 years ago
Option D? Nah, that's just throwing the baby out with the bathwater. Let's fix the data, not discard it!
CO
Corinne
2 years ago
Option D? Nah, that's just throwing the baby out with the bathwater. Let's fix the data, not discard it!
ON
Ona
2 years ago
C) Change the method of binning to fixed width.
MA
Mary
2 years ago
B) Nothing, because the field is not the outcome variable.
LE
Lelia
2 years ago
A) Remove the outlier rows.
SH
Sherell
2 years ago
I agree with Francisca, removing the outlier rows would be the best option.
KI
King
2 years ago
I'd go with C. Changing the binning method could help handle the skewed data better.
VI
Viva
2 years ago
I'm not sure, B seems reasonable too. If the field isn't the outcome variable, why bother with the outliers?
GL
Glendora
2 years ago
I'm not sure, B seems reasonable too. If the field isn't the outcome variable, why bother with the outliers?
CA
Cammy
2 years ago
B) Nothing, because the field is not the outcome variable.
DA
Daisy
2 years ago
A) Remove the outlier rows.
IS
Isabelle
2 years ago
I disagree, I believe we should change the method of binning to fixed width.
AR
Arthur
2 years ago
Definitely option A. Removing outliers is crucial for a clean and reliable training dataset.
CH
Chaya
2 years ago
Outliers can really skew the data, so it's best to get rid of them.
VI
Vivan
2 years ago
I agree, removing outliers is important for accurate results.
FR
Francisca
2 years ago
I think we should remove the outlier rows.