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Pegasystems PEGACPDS88V1 Exam - Topic 1 Question 11 Discussion

Configuring an adaptive model involves selecting the potential predictors. How many potential predictors are recommended for an adaptive model?
A) At least 100 fields to reach an acceptable level of model performance
B) All fields that have been predictive in the past
C) All available uncorrected fields
D) Up to 100 fields to limit the impact on model speed

Pegasystems PEGACPDS88V1 Exam - Topic 1 Question 11 Discussion

Actual exam question for Pegasystems's PEGACPDS88V1 exam
Question #: 11
Topic #: 1
[All PEGACPDS88V1 Questions]

Configuring an adaptive model involves selecting the potential predictors. How many potential predictors are recommended for an adaptive model?

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Suggested Answer: A

As a data scientist, a valid reason to adjust the default response timeout in a prediction is tosuit the use case.


by Julio at May 08, 2024, 08:35 AM

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Kallie
7 months ago
Nah, I prefer the idea of using all available fields for better accuracy.
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Tamie
8 months ago
I think all past predictive fields should be included, right?
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Sharan
8 months ago
Wait, are we really limited to 100 fields? That seems low.
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Rodney
8 months ago
Totally agree, too many fields can slow things down!
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Thurman
8 months ago
I heard at least 100 fields is the way to go for good performance.
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Carry
9 months ago
I vaguely recall that having too many predictors can slow down the model, so I might lean towards option D, but I'm not completely confident.
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Vivan
9 months ago
I feel like we discussed using all predictive fields in class, but that could lead to overfitting, right?
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Sylvie
9 months ago
I think I saw a similar question where it mentioned limiting predictors to improve speed, so maybe option D makes sense?
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Berry
9 months ago
I remember something about needing a balance between too many predictors and model performance, but I'm not sure if it's exactly 100.
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India
9 months ago
I'm pretty confident that the recommended approach is to use all available uncorrected fields. That should give the best model performance.
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Rana
9 months ago
Okay, I think the key here is to balance model performance with computational efficiency. I'll go with option D to limit the impact on model speed.
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Roslyn
9 months ago
I'm not entirely sure about the recommended number of predictors. I'll have to review my notes and try to reason through the options.
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Diego
9 months ago
Hmm, this is a tricky one. I'll need to think carefully about the trade-offs between model performance and speed.
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Candra
10 months ago
Wait, I'm a little confused. Are we talking about the fraudster buying something for themselves and then expensing it to the company? Or is it more about the fraudster submitting fake invoices for things the company didn't actually purchase? I want to make sure I understand the scenario before I answer.
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Ellsworth
10 months ago
Whew, this is a lot of information to take in! But I feel confident that if I break it down and focus on the core principles, I should be able to figure out which option would benefit the most from the change in dividend growth rate. Gotta stay calm and methodical here.
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Novella
1 year ago
100 fields? That's a lot of data to crunch. I hope my computer can handle it, or I might as well just run the model by hand.
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Holley
1 year ago
D) Up to 100 fields to limit the impact on model speed
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Candida
1 year ago
100 fields does seem like a lot, but it's important for accuracy.
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Stephen
1 year ago
A) At least 100 fields to reach an acceptable level of model performance
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Nadine
1 year ago
Up to 100 fields? That's a lot, but at least it's a reasonable limit. I wonder how that impacts the model's performance and speed.
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Nieves
1 year ago
User 3: Nieves, that makes sense. It's important to balance performance and speed.
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Lettie
1 year ago
User 2: Lettie, I think it's to limit the impact on model speed.
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Keva
1 year ago
User 1: Up to 100 fields seems like a lot, but it's a reasonable limit.
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Laticia
1 year ago
Uncorrected fields? That sounds like a recipe for disaster. I'll have to do some more research on best practices for adaptive model configuration.
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Rhea
1 year ago
User 2: I agree, having too many uncorrected fields could definitely lead to issues. It's important to find the right balance.
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Raul
1 year ago
User 1: I think it's best to limit the potential predictors to up to 100 fields to maintain model speed.
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Casie
1 year ago
But wouldn't it be better to include all fields that have been predictive in the past for better accuracy?
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Norah
1 year ago
I'm not sure about using all fields that were predictive in the past. Shouldn't we focus on the most relevant ones for the current situation?
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France
1 year ago
D) Up to 100 fields to limit the impact on model speed
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Regenia
1 year ago
I agree, using all past predictors may not be the best approach for current situations.
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Sharee
1 year ago
B) All fields that have been predictive in the past
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Yaeko
1 year ago
A) At least 100 fields to reach an acceptable level of model performance
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Glory
1 year ago
Wow, 100 fields? That seems a bit excessive. I wonder if there's a more efficient way to configure an adaptive model.
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Bernardine
1 year ago
I agree with Paulene, having too many predictors can slow down the model.
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Paulene
1 year ago
I think the answer is D) Up to 100 fields to limit the impact on model speed.
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