A CRM Analytics consultant is reviewing results from an Einstein Discovery story with a business user. They agree with the findings but notice that none of the fields used in the story have a correlation value greater than 4%. The client is now concerned that the model
may not be good enough to deploy.
Which action should the consultant take?
Suggested Answer: A
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22 comments · newest first
CH
Chauncey
24 days ago
C could be risky if we don't understand the current model first.
GA
Gaynell
29 days ago
I feel like A is the most logical. We need to dig deeper into the data.
EL
Ellsworth
1 month ago
True, but if the data isn't strong, no model will work well. A is safer.
MA
Ma
1 month ago
I'm leaning towards B. Adjusting accuracy settings could improve results.
TA
Tabetha
1 month ago
I agree, but option C could also work. A different algorithm might reveal insights.
MA
Marcelle
2 months ago
I think option A is the best. More data can help find better correlations.
JE
Jerry
2 months ago
C is interesting. Sometimes a fresh algorithm can reveal hidden patterns.
BR
Brianne
2 months ago
I feel like A is the safest bet. New data could change everything.
RE
Regenia
2 months ago
True, but B could help refine the model. It's worth considering.
RO
Rory
2 months ago
Editing settings in B seems risky. Might not solve the core issue.
ME
Melissa
3 months ago
I'm leaning towards A too. We need stronger data connections.
LE
Lezlie
3 months ago
I agree, but option C could also work. A better algorithm might improve results.
BE
Beth
3 months ago
I think option A is the best. More data can help find better correlations.
AS
Ashlee
3 months ago
Using a different algorithm could make a big difference!
CR
Crista
3 months ago
Editing accuracy settings might not help much.
KR
Kristel
3 months ago
Wait, can we really trust a model with such weak correlations?
IZ
Izetta
4 months ago
Totally agree, they need to find better data!
ME
Merri
4 months ago
Correlation values under 4% are pretty low.
NY
Nydia
4 months ago
I feel like using the right algorithm is important, but if the data isn't strong enough, it might not make a difference. I'm a bit confused about the best approach here.
AD
Adelina
4 months ago
This reminds me of a practice question where we had to choose between updating the model or finding new data. I think finding additional data is usually the better first step.
BE
Belen
4 months ago
I'm not entirely sure, but I think editing the model accuracy settings might not really address the underlying issue with the data itself.
CH
Chu
4 months ago
I remember we discussed how low correlation values can indicate weak predictors, so maybe identifying additional data could help strengthen the model.