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When working with smaller data sets (N<200), which method is preferred to perform honest assessment?
Suggested Answer: C
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43 comments · newest first
PA
Pa
12 months ago
K-fold is preferred for N<200, just saying!
JE
Jean
12 months ago
Cross validation using quartiles is a solid choice.
CH
Charlette
1 year ago
Wait, what's AIC goodness of fit? Sounds fancy!
CE
Ceola
1 year ago
I disagree, the 40-30-30 split can work too.
BR
Brice
1 year ago
K-fold cross validation is usually the best for small datasets!
CO
Colene
1 year ago
I vaguely recall that splitting the data into 40% training and 30% validation might not be ideal for smaller datasets, but I can't remember the specifics.
RA
Raylene
1 year ago
I feel like using the AIC goodness of fit statistic could be relevant, but it doesn't directly address the assessment method for small datasets.
MA
Marla
1 year ago
I remember practicing with a question that emphasized the importance of validation techniques for small samples, so I might lean towards option B.
DO
Donte
1 year ago
I think K-fold cross validation is usually the go-to for smaller datasets, but I'm not entirely sure if it's the best choice here.
GA
Gaynell
1 year ago
I vaguely remember something about using quartiles for validation, but I’m not confident that’s the right approach for this question.
KR
Kristin
1 year ago
I’m leaning towards option B, K-fold, since it allows for better use of the data we have, but I can't recall all the details.
HO
Hoa
1 year ago
I remember practicing with the 40-30-30 split, but it feels like K-fold might give a better assessment with fewer data points.
BO
Bo
1 year ago
I think K-fold cross validation is often recommended for smaller datasets, but I’m not entirely sure if it’s the best choice here.
TR
Tran
1 year ago
Okay, let's see. Privacy is about restricting access and keeping information confidential, while security is about protecting the integrity and availability of the data. I think I know which answer choice captures that difference, but I'll double-check to make sure.
TH
Thea
1 year ago
The Development Team is definitely responsible for estimating the Product Backlog items. That's a core part of their role in Scrum.
TO
Toi
1 year ago
I think I know the answer to this one. Using Springs to control the simulation if it gets erratic sounds like the right approach.
JE
Jennifer
1 year ago
I'm pretty confident this is a problem with the 802.1X authentication policy. That's usually the culprit when you can't connect to a WPA2-Enterprise network.
RO
Rory
1 year ago
I remember something about avoiding conflicts of interest – I believe that's a key role of directors, right?
GI
Gilberto
1 year ago
Option E) Throw darts at the wall and go with whatever sticks. Scientific method, am I right?
MA
Mattie
1 year ago
A) Training: 40% Validation: 30% Testing: 30%
CO
Cortney
1 year ago
B) K-fold cross validation is a good choice.
GR
Gregoria
1 year ago
B) K-fold cross validation
LE
Leanora
1 year ago
A) 40-30-30 split? That's just begging to overfit on the training set. I'd much rather go with the flexibility of K-fold.
ED
Edmond
1 year ago
A) Exactly, it helps prevent bias in the model evaluation process.
MI
Michal
1 year ago
B) It's important to avoid overfitting, so K-fold is the way to go.
JO
Jose
1 year ago
A) I agree, K-fold cross validation is definitely more reliable.
GE
Geoffrey
1 year ago
C) Cross validation using the 4th quartile? Sounds like a bit of a gimmick to me. I'll play it safe with B.
VA
Valda
1 year ago
Yeah, K-fold cross validation is a more reliable method for honest assessment.
OL
Olene
1 year ago
I think K-fold cross validation is the way to go for smaller data sets.
LI
Lindsey
1 year ago
I agree, using the 4th quartile seems risky. K-fold cross validation is a safer bet.
MA
Ma
1 year ago
D) AIC goodness of fit? Isn't that more for model selection than performance assessment? I'd stick with the tried and true K-fold approach.
PA
Patria
2 years ago
B) K-fold cross validation sounds like the way to go for smaller data sets. Keeps the validation honest without sacrificing too much of the training data.
SH
Sherrell
1 year ago
Using K-fold cross validation can definitely improve the assessment process.
MO
Moira
1 year ago
I think it strikes a good balance between training and validation.
HE
Heidy
1 year ago
It helps in keeping the validation process honest.
NI
Niesha
1 year ago
I agree, K-fold cross validation is a good choice for smaller data sets.
CU
Curt
1 year ago
It's definitely a reliable method for honest assessment with smaller data sets.
FL
Fletcher
1 year ago
I've used it before and it worked well for me.
MO
Mohammad
1 year ago
It helps in keeping the validation honest while still utilizing the data efficiently.
SH
Sherron
1 year ago
I agree, K-fold cross validation is a good choice for smaller data sets.
LA
Latosha
2 years ago
I prefer using the AIC goodness of fit statistic. It provides a good measure of model performance.
GE
Georgiann
2 years ago
I agree with Kent. K-fold cross validation helps in getting a more accurate assessment.
KE
Kent
2 years ago
I think when working with smaller data sets, K-fold cross validation is preferred.