{"id":43631,"date":"2024-04-15T21:32:12","date_gmt":"2024-04-15T21:32:12","guid":{"rendered":"https:\/\/exam.pscnotes.com\/mcq\/?p=43631"},"modified":"2024-04-15T21:32:12","modified_gmt":"2024-04-15T21:32:12","slug":"what-is-the-primary-purpose-of-t-sne-t-distributed-stochastic-neighbor-embedding-in-data-visualization","status":"publish","type":"post","link":"https:\/\/exam.pscnotes.com\/mcq\/what-is-the-primary-purpose-of-t-sne-t-distributed-stochastic-neighbor-embedding-in-data-visualization\/","title":{"rendered":"What is the primary purpose of “t-SNE” (t-distributed stochastic neighbor embedding) in data visualization?"},"content":{"rendered":"

[amp_mcq option1=”To create decision boundaries” option2=”To perform feature scaling” option3=”To calculate the mean squared error” option4=”To reduce the dimensionality of data while preserving pairwise similarities” correct=”option4″]<\/p>\n

The correct answer is D. To reduce the dimensionality of data while preserving pairwise similarities.<\/p>\n

t-SNE is a dimensionality reduction technique that is often used for data visualization. It works by finding a low-dimensional representation of the data that preserves the pairwise similarities between data points. This means that data points that are similar in the high-dimensional space will also be similar in the low-dimensional space. This can be useful for visualizing data, as it can help to make patterns in the data more apparent.<\/p>\n

A decision boundary is a line or surface that separates two classes of data. t-SNE is not used to create decision boundaries.<\/p>\n

Feature scaling is a technique that is used to make features in a dataset have similar scales. This can be useful for some machine learning algorithms. t-SNE is not used for feature scaling.<\/p>\n

The mean squared error is a measure of the difference between two sets of data. t-SNE is not used to calculate the mean squared error.<\/p>\n

In conclusion, the primary purpose of t-SNE in data visualization is to reduce the dimensionality of data while preserving pairwise similarities.<\/p>\n","protected":false},"excerpt":{"rendered":"

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