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Question 287 - MLS-C01 discussion

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A company is building a predictive maintenance model for its warehouse equipment. The model must predict the probability of failure of all machines in the warehouse. The company has collected 10.000 event samples within 3 months. The event samples include 100 failure cases that are evenly distributed across 50 different machine types.

How should the company prepare the data for the model to improve the model's accuracy?

A.

Adjust the class weight to account for each machine type.

Answers
A.

Adjust the class weight to account for each machine type.

B.

Oversample the failure cases by using the Synthetic Minority Oversampling Technique (SMOTE).

Answers
B.

Oversample the failure cases by using the Synthetic Minority Oversampling Technique (SMOTE).

C.

Undersample the non-failure events. Stratify the non-failure events by machine type.

Answers
C.

Undersample the non-failure events. Stratify the non-failure events by machine type.

D.

Undersample the non-failure events by using the Synthetic Minority Oversampling Technique (SMOTE).

Answers
D.

Undersample the non-failure events by using the Synthetic Minority Oversampling Technique (SMOTE).

Suggested answer: B

Explanation:

In predictive maintenance, when a dataset is imbalanced (with far fewer failure cases than non-failure cases), oversampling the minority class helps the model learn from the minority class effectively. The Synthetic Minority Oversampling Technique (SMOTE) generates synthetic samples for the minority class by creating data points between existing minority class instances. This can enhance the model's ability to recognize failure patterns, particularly in imbalanced datasets.

SMOTE increases the effective presence of failure cases in the dataset, providing a balanced learning environment for the model. This is more effective than undersampling, which would risk losing important non-failure data.

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asked 31/10/2024
Leila Bekirova
39 questions
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