Using classification with a file-dataset, if we increase the number of samples, would it reduce the cost / speed somehow?
To my understanding, with more samples, the search will be faster right? But then I’m not sure how the model pick “selected examples”, because I think more selected examples means higher cost & inference time.
TLDR: How to reduce classification price and speed while keeping / improving performance?