BlackFriday 2024! Hurry Up, Grab the Special Discount - Save 25% - Ends In 00:00:00 Coupon code: SAVE25
Welcome to Pass4Success

- Free Preparation Discussions

CertNexus Exam AIP-210 Topic 2 Question 31 Discussion

Actual exam question for CertNexus's AIP-210 exam
Question #: 31
Topic #: 2
[All AIP-210 Questions]

An organization sells house security cameras and has asked their data scientists to implement a model to detect human feces, as distinguished from animals, so they can alert th customers only when a human gets close to their house.

Which of the following algorithms is an appropriate option with a correct reason?

Show Suggested Answer Hide Answer
Suggested Answer: D

Neural network models are suitable for classification problems with a large number of features, because they can learn complex and non-linear patterns from high-dimensional data. They can also handle image data, which is likely to be the input for the human face detection problem. Neural networks can also be trained using transfer learning, which can leverage pre-trained models on similar tasks and improve the accuracy and efficiency of the model. Reference: [Neural network - Wikipedia], [Transfer Learning - Machine Learning's Next Frontier]


Contribute your Thoughts:

Valentin
24 days ago
k-means clustering? For real? How is that supposed to help identify human poop? What is this, a scavenger hunt?
upvoted 0 times
...
Margo
26 days ago
Wait, we're detecting human feces near people's houses? I'm not sure I want to be a part of this project...
upvoted 0 times
...
Pearlie
27 days ago
Neural networks are always the answer, right? Gotta go with the deep learning hype train on this one.
upvoted 0 times
Cruz
5 days ago
C: Decision tree could be a good choice, it's simple and effective for classification.
upvoted 0 times
...
Sherill
10 days ago
B: But logistic regression might work too, our data seems linearly separable.
upvoted 0 times
...
Abraham
13 days ago
A: I think neural network model is the way to go. It can handle complex features.
upvoted 0 times
...
...
Norah
1 months ago
Logistic regression, because I bet the data is nice and linear. Who doesn't love a good line-fitting model?
upvoted 0 times
Sueann
6 days ago
I agree, logistic regression seems like the best option here.
upvoted 0 times
...
Amira
10 days ago
C) Logistic regression, because this is a classification problem and our data is linearly separable.
upvoted 0 times
...
Maxima
15 days ago
A) A decision tree algorithm, because the problem is a classification problem with a small number of features.
upvoted 0 times
...
...
Mendy
1 months ago
I'm not sure, but I think C) logistic regression could also work since our data is linearly separable.
upvoted 0 times
...
Curt
2 months ago
I agree with Lillian. It's a classification problem with a small number of features.
upvoted 0 times
...
Merilyn
2 months ago
A decision tree seems like the way to go here - simple, straightforward, and just what the problem calls for.
upvoted 0 times
Kassandra
21 days ago
I'm leaning towards a neural network model for this, considering the large number of features in the problem.
upvoted 0 times
...
Shala
24 days ago
I see your point, but I still believe a decision tree is the most suitable choice in this scenario.
upvoted 0 times
...
Vallie
29 days ago
I'm leaning towards a neural network model for this task, considering the large number of features.
upvoted 0 times
...
Floyd
1 months ago
I think logistic regression could also be a good option since the data is linearly separable.
upvoted 0 times
...
Tony
1 months ago
I think logistic regression could also be a good option since the data is linearly separable.
upvoted 0 times
...
Lili
1 months ago
I agree, a decision tree would work well for this problem.
upvoted 0 times
...
Maryrose
1 months ago
I agree, a decision tree would work well for this problem.
upvoted 0 times
...
...
Lillian
2 months ago
I think the appropriate option is A) decision tree algorithm.
upvoted 0 times
...

Save Cancel