Topic 1 Question 68
2 つ選択An agency collects census information within a country to determine healthcare and social program needs by province and city. The census form collects responses for approximately 500 questions from each citizen. Which combination of algorithms would provide the appropriate insights?
The factorization machines (FM) algorithm
The Latent Dirichlet Allocation (LDA) algorithm
The principal component analysis (PCA) algorithm
The k-means algorithm
The Random Cut Forest (RCF) algorithm
解説
The PCA and K-means algorithms are useful in collection of data using census form.
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コメント(5)
C: (OK) Use PCA for reducing number of variables. Each citizen's response should have answer for 500 questions, so it should have 500 variables D: (OK) Use K-means clustering
A: (Not OK) Factorization Machines Algorithm is usually used for tasks dealing with high dimensional sparse datasets B: (Not OK) The Latent Dirichlet Allocation (LDA) algorithm should be used for task dealing topic modeling in NLP E: (Not OK) Random Cut Forest should be used for detecting anormal in data
👍 29HaiHN2021/10/29- 👍 11hans12342021/10/25
- 正解だと思う選択肢: CD
C. The principal component analysis (PCA) algorithm D. The k-means algorithm
PCA is a dimensionality reduction technique that can be used to identify the underlying structure of the census data. This algorithm can help to identify the most important questions and provide an overview of the relationship between the questions and the responses.
K-means is an unsupervised learning algorithm that can be used to segment the population into different groups based on their responses to the census questions. This algorithm can help to determine the healthcare and social program needs by province and city based on the responses collected from each citizen.
These algorithms can help to provide insights into the patterns and relationships within the census data, which can inform decision making for healthcare and social program planning.
👍 3AjoseO2023/02/10
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