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CertNexus AIP-210 Exam - Topic 6 Question 21 Discussion

Actual exam question for CertNexus's AIP-210 exam
Question #: 21
Topic #: 6
[All AIP-210 Questions]

Given a feature set with rows that contain missing continuous values, and assuming the data is normally distributed, what is the best way to fill in these missing features?

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Suggested Answer: B

A support-vector machine (SVM) is a supervised learning algorithm that can be used for classification or regression problems. An SVM tries to find an optimal hyperplane that separates the data into different categories or classes. However, sometimes the data is not linearly separable, meaning there is no straight line or plane that can separate them. In such cases, a polynomial kernel can help improve the prediction of the SVM by transforming the data into a higher-dimensional space where it becomes linearly separable. A polynomial kernel is a function that computes the similarity between two data points using a polynomial function of their features.


by Coleen at Jun 23, 2024, 06:11 AM

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Brendan
5 months ago
C makes sense, especially with normal distribution!
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Cletus
5 months ago
Definitely not D, losing entire columns is too extreme.
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Doug
5 months ago
Wait, random values (B)? That sounds risky!
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Anglea
6 months ago
I disagree, deleting rows (A) can lose too much data.
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Barney
6 months ago
C is the best option, averages work well!
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Sharen
6 months ago
Deleting columns seems extreme unless they have too many missing values, but I don’t recall if that’s a good strategy for this scenario.
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Lawanda
6 months ago
I practiced a similar question where using the average was suggested, especially for normally distributed data. That might be the right choice here.
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Edelmira
6 months ago
I’m a bit unsure, but I think filling in with random values could introduce more noise into the data.
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Isidra
6 months ago
I remember discussing how deleting rows can lead to loss of valuable data, so I think that's not the best option.
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Doretha
6 months ago
Hmm, I'm not sure about this one. Filling in with random values seems like it could introduce a lot of noise. I'll need to think through the implications of each option more carefully.
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Silva
6 months ago
I'm pretty confident that filling in the missing values with the feature averages is the best approach, given the data is normally distributed. That should preserve the overall distribution.
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Edda
6 months ago
Okay, I think I know the right way to handle this. I'll go with option C and fill in the missing values with the feature averages.
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Casey
7 months ago
Hmm, this is a tricky one. I'll need to think carefully about the pros and cons of each approach.
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Lettie
7 months ago
I'm a bit confused here. Deleting rows or columns with missing data seems like it could lead to a lot of information loss. Maybe there's a better way to handle this.
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Antione
7 months ago
This question seems straightforward, but I want to make sure I understand the key concepts before answering. The focus is on measurement criteria that are not relevant for performance audits.
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Andra
7 months ago
Hmm, I'm a bit unsure about this one. I'll need to think through the connections between the different labor laws and agencies to figure this out.
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Laura
7 months ago
Okay, let me see. The question is asking about the Tesla V100 or P100 GPUs, so I'll need to check the compatibility of those models with the G560.
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Melynda
7 months ago
I think using the Cisco FTD IP as the proxy might have come up in a different scenario we studied. I'm not certain, but it rings a bell.
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Denise
7 months ago
I've got a good feeling about this one. Based on my understanding of Kafka producers, the two most likely exceptions are BrokerNotAvailableException and SerializationException.
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Luisa
12 months ago
I've got a brilliant idea - why not just fill in the missing values with the average of the entire dataset, and then add a random number to it? That way, it'll be like a surprise every time!
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Ellsworth
11 months ago
User 3: I agree, adding random values might not be the best approach for filling in missing features in a normally distributed dataset.
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Rosendo
11 months ago
User 2: I think so too. It might be better to just fill in the missing values with the average of the entire dataset to maintain the distribution.
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Genevieve
12 months ago
User 1: That's an interesting idea, but wouldn't adding a random number introduce noise into the data?
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Tammara
12 months ago
Ah, the age-old dilemma of missing data. Deleting rows or columns seems a bit drastic, but I suppose if you're feeling brave, you could always roll the dice and see what happens.
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Monroe
1 year ago
B is a terrible idea. Filling in with random values? That's just asking for trouble. Might as well flip a coin while you're at it.
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Gilma
11 months ago
C) Fill in missing features with the average of observed values for that feature in the entire dataset.
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Susy
11 months ago
A) Delete entire rows that contain any missing features.
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Gerry
12 months ago
C) Fill in missing features with the average of observed values for that feature in the entire dataset.
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Mendy
1 year ago
D? Are you kidding me? Deleting entire columns with missing data is way too extreme. That's like throwing the baby out with the bathwater.
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Estrella
1 year ago
C is the way to go! Filling in with the average of observed values makes the most sense when dealing with a normal distribution.
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Rosann
11 months ago
I think deleting entire rows with missing features is too drastic, filling in with the average is more reasonable.
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Aide
11 months ago
I agree, filling in with the average of observed values is the best approach.
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Earnestine
1 year ago
I think filling in missing features with random values for that feature in the training set could introduce bias, so I would go with option C.
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Jesusita
1 year ago
I disagree, I believe we should delete entire rows that contain any missing features.
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Albina
1 year ago
I think we should fill in missing features with the average of observed values for that feature in the entire dataset.
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