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

- Free Preparation Discussions

HP Exam HPE2-N69 Topic 4 Question 32 Discussion

Actual exam question for HP's HPE2-N69 exam
Question #: 32
Topic #: 4
[All HPE2-N69 Questions]

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

Show Suggested Answer Hide Answer
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.


Contribute your Thoughts:

Melvin
5 months ago
I agree. Overfitting can really mess up the predictions on unseen data.
upvoted 0 times
...
Domitila
6 months ago
Makes sense, we want them to generalize well on new data.
upvoted 0 times
...
Lashunda
6 months ago
Yeah, overfitting makes models very specific to training data.
upvoted 0 times
...
Sommer
6 months ago
It's definitely C. The team wants to avoid training models until they perform poorly on new data.
upvoted 0 times
...
Domitila
6 months ago
Oh, that sounds important.
upvoted 0 times
...
Donte
6 months ago
The question is about avoiding overfitting models.
upvoted 0 times
...

Save Cancel