What is the primary purpose of “t-SNE” (t-distributed stochastic neighbor embedding) in data visualization?

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.

A decision boundary is a line or surface that separates two classes of data. t-SNE is not used to create decision boundaries.

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.

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.

In conclusion, the primary purpose of t-SNE in data visualization is to reduce the dimensionality of data while preserving pairwise similarities.