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    Question

    Which of the following algorithms is best suited for

    handling high-dimensional and sparse datasets, commonly encountered in text processing and natural language processing tasks?
    A K-Nearest Neighbors (KNN) Correct Answer Incorrect Answer
    B Decision Trees Correct Answer Incorrect Answer
    C Support Vector Machines (SVM) Correct Answer Incorrect Answer
    D Latent Dirichlet Allocation (LDA) Correct Answer Incorrect Answer
    E Linear Regression Correct Answer Incorrect Answer

    Solution

    LDA is a probabilistic topic modeling algorithm that is particularly well-suited for handling high-dimensional and sparse datasets. It is commonly used in text processing and natural language processing tasks to discover latent topics within a collection of documents. LDA can automatically identify patterns and relationships in large corpora, making it a valuable tool for analyzing unstructured textual data. The other options (A) K-Nearest Neighbors, (B) Decision Trees, (C) Support Vector Machines, and (E) Linear Regression are not specifically designed for handling sparse and high-dimensional data, although they have their applications in various other data analysis tasks.

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