What are the key parameters for t-SNE?
An example of an AI chatbot conversation about t-SNE parameters and their impact on data visualization.
What are the key parameters for t-SNE?
The key parameters for t-SNE include perplexity, learning rate, and number of iterations. Would you like to know more about each?
Yes, please explain perplexity.
Perplexity is a measure of the number of nearest neighbors considered in the algorithm. It affects the balance between local and global data structure.
How does the learning rate influence the results?
The learning rate controls how much the algorithm adjusts the embedding at each iteration. A too high rate can lead to divergence, while a too low rate can slow convergence.
What about the number of iterations?
The number of iterations determines how long the algorithm runs. More iterations can lead to better convergence but also increase computation time.
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