How do we evaluate the quality of embeddings in machine learning?

How do we evaluate the quality of embeddings in machine learning?

Evaluating embeddings typically involves assessing their performance on downstream tasks, such as classification or clustering.

What metrics can we use to assess embeddings?

Common metrics include cosine similarity, Euclidean distance, and task-specific metrics like accuracy or F1 score.

Can you explain how cosine similarity works?

Cosine similarity measures the cosine of the angle between two vectors, indicating how similar they are regardless of their magnitude.

Are there any visualization techniques for embeddings?

Yes, techniques like t-SNE and PCA can help visualize high-dimensional embeddings in 2D or 3D space.