Topic 1 Question 217
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?
Adjust the class weight to account for each machine type.
Oversample the failure cases by using the Synthetic Minority Oversampling Technique (SMOTE).
Undersample the non-failure events. Stratify the non-failure events by machine type.
Undersample the non-failure events by using the Synthetic Minority Oversampling Technique (SMOTE).
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コメント(3)
- 正解だと思う選択肢: B
oversample the minority class
👍 6blt232023/02/03 - 正解だと思う選択肢: B
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.
👍 2AjoseO2023/02/20 - 正解だと思う選択肢: B
B. Oversample the failure cases by using the Synthetic Minority Oversampling Technique (SMOTE).
Since the number of failure cases is relatively small, oversampling the failure cases using techniques like SMOTE can help balance the class distribution and prevent the model from being biased towards the majority class. SMOTE creates synthetic samples for the minority class by interpolating new samples between existing ones. This will help improve the model's accuracy in predicting failure cases. Adjusting class weights (A) or undersampling (C, D) may not be as effective in this scenario.
👍 2oso03482023/03/04
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