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HP Exam HPE2-N69 Topic 1 Question 34 Discussion

Actual exam question for HP's HPE2-N69 exam
Question #: 34
Topic #: 1
[All HPE2-N69 Questions]

A customer mentions that the ML team wants to avoid overfitting models. What does this mean?

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Suggested Answer: C

Overfitting occurs when a model is trained too closely on the training data, leading to a model that performs very well on the training data but poorly on new data. This is because the model has been trained too closely to the training data, and so cannot generalize the patterns it has learned to new data. To avoid overfitting, the ML team needs to ensure that their models are not overly trained on the training data and that they have enough generalization capacity to be able to perform well on new data.


by Queenie at May 23, 2024, 03:29 AM

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Rana
3 months ago
Option B is just silly. Spending less time on the code and more on training? That's not how it works.
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Yolande
3 months ago
Haha, option D is a good one. The team should worry more about overfitting than the available CPUs.
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Gail
3 months ago
I agree with Irving. Overfitting is a common problem in machine learning that the team wants to avoid.
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Irving
3 months ago
Option C is the correct answer. Overfitting means the model performs too well on the training data but doesn't generalize well to new, unseen data.
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Colene
2 months ago
That makes sense, we want our models to be able to make accurate predictions on new data.
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Elmira
2 months ago
C) The team wants to avoid training models to the point where they perform less well on new data.
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Ceola
2 months ago
That makes sense, we want our models to be able to make accurate predictions on new data.
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Elvera
2 months ago
A: Exactly, they want the model to be able to make accurate predictions on new data.
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Nakisha
2 months ago
B: So they want to make sure the model doesn't just memorize the training data?
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Pamella
2 months ago
A: The team wants to avoid training models to the point where they perform less well on new data.
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Johnson
2 months ago
B: They want to avoid training models to the point where they perform less well on new data.
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Rupert
2 months ago
A: Why does the ML team want to avoid overfitting models?
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Hubert
3 months ago
C) The team wants to avoid training models to the point where they perform less well on new data.
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