MLA-C01 · Question #227
A healthcare company wants to detect irregularities in patient vital signs that could indicate early signs of a medical condition. The company has an unlabeled dataset that includes patient health rec
The correct answer is D. Use the Amazon SageMaker AI Random Cut Forest (RCF) algorithm. Set num_trees to greater. Random Cut Forest is purpose-built for anomaly detection on unlabeled data, making it well- suited for finding irregular vital-sign patterns that could indicate emerging issues. Increasing the number of trees improves anomaly scoring stability and robustness by averaging results
Question
A healthcare company wants to detect irregularities in patient vital signs that could indicate early signs of a medical condition. The company has an unlabeled dataset that includes patient health records, medication history, and lifestyle changes. Which algorithm and hyperparameter should the company use to meet this requirement?
Options
- AUse the Amazon SageMaker AI XGBoost algorithm. Set max_depth to greater than 100 to
- BUse the Amazon SageMaker AI k-means clustering algorithm. Set k to determine the number of
- CUse the Amazon SageMaker AI DeepAR algorithm. Set epochs to the number of training
- DUse the Amazon SageMaker AI Random Cut Forest (RCF) algorithm. Set num_trees to greater
How the community answered
(36 responses)- A3% (1)
- B14% (5)
- C6% (2)
- D78% (28)
Explanation
Random Cut Forest is purpose-built for anomaly detection on unlabeled data, making it well- suited for finding irregular vital-sign patterns that could indicate emerging issues. Increasing the number of trees improves anomaly scoring stability and robustness by averaging results across more random partitions.
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