Topic 1 Question 263
2 つ選択A pharmaceutical company performs periodic audits of clinical trial sites to quickly resolve critical findings. The company stores audit documents in text format. Auditors have requested help from a data science team to quickly analyze the documents. The auditors need to discover the 10 main topics within the documents to prioritize and distribute the review work among the auditing team members. Documents that describe adverse events must receive the highest priority.
A data scientist will use statistical modeling to discover abstract topics and to provide a list of the top words for each category to help the auditors assess the relevance of the topic.
Which algorithms are best suited to this scenario?
Latent Dirichlet allocation (LDA)
Random forest classifier
Neural topic modeling (NTM)
Linear support vector machine
Linear regression
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コメント(6)
Although you can use both the Amazon SageMaker NTM and LDA algorithms for topic modeling, they are distinct algorithms and can be expected to produce different results on the same input data.
A and C
https://docs.aws.amazon.com/sagemaker/latest/dg/ntm.html https://docs.aws.amazon.com/sagemaker/latest/dg/lda.html
👍 3worldboss2023/07/03- 正解だと思う選択肢: AC
A. YES B. NO - for classification C. YES D. NO - for classification E. NO - for classification
👍 2loict2023/09/12 - 正解だと思う選択肢: AC
AC for topics
👍 1awsarchitect52023/07/25
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