MLS-C01 · Question #245
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…
The correct answer is B. Oversample the failure cases by using the Synthetic Minority Oversampling Technique (SMOTE). The data provided is imbalanced, with only 100 failure cases out of 10,000 event samples. Therefore, it is important to address this imbalance to improve the accuracy of the predictive maintenance model.
Question
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?
Options
- AAdjust the class weight to account for each machine type.
- BOversample the failure cases by using the Synthetic Minority Oversampling Technique (SMOTE).
- CUndersample the non-failure events. Stratify the non-failure events by machine type.
- DUndersample the non-failure events by using the Synthetic Minority Oversampling Technique
How the community answered
(40 responses)- A5% (2)
- B75% (30)
- C13% (5)
- D8% (3)
Explanation
The data provided is imbalanced, with only 100 failure cases out of 10,000 event samples. Therefore, it is important to address this imbalance to improve the accuracy of the predictive maintenance model.
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