I am new to data analysis and I am trying to run a kNN classifier on a lung cancer dataset with multiple attributes. For all k values I tasted (1 to 10), I obtain the same accuracy when using either Manhattan or Euclidean distances. I am not sure what this is saying about the data? Is the reason that there's no difference the fact that I don't use a large enough data set (300 entries with a 70% training split)?


  • $\begingroup$ Should be due to Imbalance. What is the Class ratio? Or maybe the boundary is very clear and simple(less likely). $\endgroup$ – 10xAI Dec 5 '20 at 16:32

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