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