Which combination of algorithms would provide the appropriate insights?
(Choose two.)
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
Explanations:
Factorization Machines (FM) are used for recommender systems and collaborative filtering, not for extracting census insights or dimensionality reduction.
Latent Dirichlet Allocation (LDA) is used for topic modeling in text analysis, not for census data or identifying program needs.
Principal Component Analysis (PCA) is a dimensionality reduction technique that helps reduce the complexity of large datasets while retaining important features. It can be used to analyze census data by reducing the number of variables.
K-means is a clustering algorithm that can identify patterns and group similar cities or provinces based on census data, making it useful for segmenting the population for healthcare and social programs.
Random Cut Forest (RCF) is a method for anomaly detection, not for extracting insights from structured census data.
It seems to me that the answer is:
The principal component analysis (PCA) algorithm
I estimate that the answer is:
The principal component analysis (PCA) algorithm