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Amazon Exam MLS-C01 Topic 3 Question 96 Discussion

Actual exam question for Amazon's MLS-C01 exam
Question #: 96
Topic #: 3
[All MLS-C01 Questions]

A machine learning engineer is building a bird classification model. The engineer randomly separates a dataset into a training dataset and a validation dataset. During the training phase, the model achieves very high accuracy. However, the model did not generalize well during validation of the validation dataset. The engineer realizes that the original dataset was imbalanced.

What should the engineer do to improve the validation accuracy of the model?

Show Suggested Answer Hide Answer
Suggested Answer: A

Stratified sampling is a technique that preserves the class distribution of the original dataset when creating a smaller or split dataset. This means that the proportion of examples from each class in the original dataset is maintained in the smaller or split dataset. Stratified sampling can help improve the validation accuracy of the model by ensuring that the validation dataset is representative of the original dataset and not biased towards any class. This can reduce the variance and overfitting of the model and increase its generalization ability. Stratified sampling can be applied to both oversampling and undersampling methods, depending on whether the goal is to increase or decrease the size of the dataset.

The other options are not effective ways to improve the validation accuracy of the model. Acquiring additional data about the majority classes in the original dataset will only increase the imbalance and make the model more biased towards the majority classes. Using a smaller, randomly sampled version of the training dataset will not guarantee that the class distribution is preserved and may result in losing important information from the minority classes. Performing systematic sampling on the original dataset will also not ensure that the class distribution is preserved and may introduce sampling bias if the original dataset is ordered or grouped by class.

References:

* Stratified Sampling for Imbalanced Datasets

* Imbalanced Data

* Tour of Data Sampling Methods for Imbalanced Classification


Contribute your Thoughts:

Blondell
5 months ago
Using a smaller, randomly sampled version of the training dataset might also be a good idea to improve generalization.
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Corinne
5 months ago
I believe acquiring additional data about the majority classes could also help.
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Gerald
5 months ago
I agree with Angella. Stratified sampling can help balance the classes.
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Angella
5 months ago
I think the engineer should perform stratified sampling.
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Victor
5 months ago
Systematic sampling? Sounds like the engineer's trying to wing it with this one.
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Paulina
4 months ago
A) Perform stratified sampling on the original dataset.
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Arlette
4 months ago
C) Use a smaller, randomly sampled version of the training dataset.
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Pura
5 months ago
B) Acquire additional data about the majority classes in the original dataset.
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Doug
5 months ago
A) Perform stratified sampling on the original dataset.
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Emiko
5 months ago
A smaller randomly sampled version of the training dataset? Now that's a real handful of feathers.
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Sherly
6 months ago
Acquiring more data for the majority classes? Sounds like a bird-brained idea to me.
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Nickie
6 months ago
Stratified sampling for the win! Gotta make sure those bird classes are evenly represented.
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Niesha
5 months ago
B) Acquire additional data about the majority classes in the original dataset.
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Armanda
5 months ago
A) Perform stratified sampling on the original dataset.
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