Numerical Computing with Python

Curse of dimensionality

KNN completely depends on distance. Hence, it is worth studying about the curse of dimensionality to understand when KNN deteriorates its predictive power with the increase in the number of variables required for prediction. This is an obvious fact that high-dimensional spaces are vast. Points in high-dimensional spaces tend to be dispersing from each other more compared with the points in low-dimensional space. Though there are many ways to check the curve of dimensionality, here we are using uniform random values between zero and one generated for 1D, 2D, and 3D space to validate this hypothesis.

In the following lines of codes, the mean distance between 1,000 observations has been calculated with the change in dimensions. ...