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?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.
According to the SEBI Act all sums realized by way of penalties can be utilized as____________
If at the adjourned meeting also, a quorum is not present within half-an-hour from the time appointed for holding meeting ____________________
What does "direct selling" refer to?
A proxy ___________ the right to speak at such meeting and shall not be entitled to vote except on a poll
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In computing the period of limitation for filing a suit the day on which it is reckoned ______.
The teacher asked the students to __________ their homework by the end of the week.
What can the Central Government do regarding the Employees' Provident Fund?
The principle as laid down in the case of Foss v. Harbottle applies to the infringement of which rights of a member of a company, under section 241 of t...
Remedial liability under the concept of Jurisprudence is based on the principle of:
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