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Amazon AIF-C01 Exam - Topic 4 Question 12 Discussion

Actual exam question for Amazon's AIF-C01 exam
Question #: 12
Topic #: 4
[All AIF-C01 Questions]

A company has built an image classification model to predict plant diseases from photos of plant leaves. The company wants to evaluate how many images the model classified correctly.

Which evaluation metric should the company use to measure the model's performance?

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Suggested Answer: B

by Sherly at Oct 24, 2024, 06:32 AM

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Rosendo
3 months ago
Learning rate? That doesn't even measure performance!
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Lemuel
3 months ago
RMSE is for regression, not classification!
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Ryann
3 months ago
Wait, why not use R-squared? Isn't that useful too?
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Desirae
4 months ago
Definitely, accuracy makes sense for classification tasks.
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Lorita
4 months ago
I think accuracy is the way to go for this!
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Myong
4 months ago
Learning rate doesn't seem like an evaluation metric at all; it's more about how the model learns during training, right?
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Ronna
4 months ago
I feel like RMSE is also not suitable here, but I can't recall why exactly. It seems more for continuous values.
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Albert
4 months ago
I'm not entirely sure, but I remember we discussed R-squared being more relevant for regression tasks, not classification.
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Pauline
5 months ago
I think the accuracy metric might be the right choice since we're looking at how many images were classified correctly.
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Charisse
5 months ago
I'm a bit unsure about this one. I know accuracy is a common metric for classification models, but I'm wondering if there might be some nuance I'm missing. Maybe I should review the definitions of the other options just to be sure.
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Annamae
5 months ago
Okay, let me think this through. Accuracy seems like the most logical choice since we're evaluating a classification model. R-squared is more for regression problems, and RMSE is also for regression. Learning rate is a hyperparameter, not an evaluation metric. I'm pretty confident accuracy is the way to go here.
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Zack
5 months ago
Accuracy makes sense, but I'm not sure if that's the only option. I might need to think through the other choices to see if any of them could also work for this type of model.
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Margurite
5 months ago
Hmm, this seems pretty straightforward. I think accuracy would be the best metric to use here since the goal is to measure how many images the model classified correctly.
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Ryann
1 year ago
D) Learning rate? Haha, that's clearly not the right metric for evaluating image classification. Might as well go with a random number generator for that one.
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Gerald
1 year ago
C) Root mean squared error (RMSE)
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Corinne
1 year ago
B) Accuracy
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Douglass
1 year ago
A) R-squared score
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Jennifer
1 year ago
I'm torn between B) Accuracy and C) RMSE. Guess I'll have to do some more research to decide.
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Tamala
1 year ago
RMSE is more suitable for regression tasks, so I would stick with Accuracy for this case.
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Olive
1 year ago
Accuracy is definitely the way to go for evaluating the performance of your image classification model.
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Mabel
1 year ago
I agree, Accuracy is a good choice for evaluating image classification models.
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Latosha
1 year ago
I think you should go with B) Accuracy. It's a common metric for classification models.
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Nilsa
1 year ago
I'd say C) Root mean squared error (RMSE) is the way to go. It gives a more nuanced view of the model's performance.
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Buck
1 year ago
Definitely going with B) Accuracy. It's the most straightforward way to measure how many images the model correctly classified.
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Nathan
1 year ago
User 4: Accuracy it is then, let's see how well the model performed.
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Alita
1 year ago
User 3: I agree, it's the most straightforward metric to use.
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In
1 year ago
User 2: Yeah, accuracy is the best way to measure performance.
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Micaela
1 year ago
User 1: I think we should go with B) Accuracy.
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Destiny
1 year ago
I agree with Dulce. Accuracy would be the best metric to measure how many images the model classified correctly.
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Dulce
1 year ago
I think the company should use Accuracy as the evaluation metric.
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