When should I use t-SNE in machine learning?

When should I use t-SNE in machine learning?

t-SNE is best used for visualizing high-dimensional data in a lower-dimensional space, especially for exploratory data analysis.

What are the advantages of using t-SNE over PCA?

t-SNE is better at preserving local structures and revealing clusters in the data, while PCA focuses on global variance.

Are there any limitations to using t-SNE?

Yes, t-SNE can be computationally intensive and may not scale well with very large datasets.

Can t-SNE be used for supervised learning?

While t-SNE is primarily used for unsupervised learning, it can help visualize the results of supervised models.

What are some common applications of t-SNE?

Common applications include visualizing clusters in image data, gene expression data, and any high-dimensional datasets.