The desired dimensionality of the output data.
The epsilon parameter controls the learning rate for the optimization algorithm.
The metric used to measure the distance between two points in the input data. Can be a function that takes two arrays of numbers and returns a number, or the string 'precomputed'.
The perplexity parameter controls the balance between preserving local and global structure in the data.
The seed used to initialize the random number generator, if applicable.
Interface for t-SNE parameters, which extends the base dimensionality reduction parameters.