Topic 1 Question 285
A company hosts a public web application on AWS. The application provides a user feedback feature that consists of free-text fields where users can submit text to provide feedback. The company receives a large amount of free-text user feedback from the online web application. The product managers at the company classify the feedback into a set of fixed categories including user interface issues, performance issues, new feature request, and chat issues for further actions by the company's engineering teams.
A machine learning (ML) engineer at the company must automate the classification of new user feedback into these fixed categories by using Amazon SageMaker. A large set of accurate data is available from the historical user feedback that the product managers previously classified.
Which solution should the ML engineer apply to perform multi-class text classification of the user feedback?
Use the SageMaker Latent Dirichlet Allocation (LDA) algorithm.
Use the SageMaker BlazingText algorithm.
Use the SageMaker Neural Topic Model (NTM) algorithm.
Use the SageMaker CatBoost algorithm.
ユーザの投票
コメント(3)
- 正解だと思う選択肢: B
B. Use the SageMaker BlazingText algorithm.
Explanation:
BlazingText for Text Classification:
SageMaker BlazingText is designed for efficient and scalable text classification tasks. It supports multi-class classification, making it suitable for the scenario where user feedback needs to be classified into fixed categories. BlazingText uses a fast implementation of the Word2Vec algorithm, making it highly performant.
👍 1aquanaveen2023/12/17 - 正解だと思う選択肢: B
BlazingText's implements a supervised multi-class, multi-label text classification algorithm.
👍 1taustin22023/12/19 - 正解だと思う選択肢: B
B. Blazing Text for text classification.
👍 1taustin22023/12/23
シャッフルモード