The Gaussian kernel is a mathematical tool that approximates a given
distribution as a sum of gaussian distributions. This is particularly useful
when we are trying to determine a distribution from a set of points.
Length (sometimes also called 'width') of the kernel.
Defaults to 0.1. This parameter essentially describes how far apart
points can be and still affect each other.
The choice for kernel length should be influenced by the average distance
of inputs. The smaller the distance, the smaller the kernel length likely
needs to be for best performance. In general, a good first guess is the
standard deviation of your predictions.
Named parameters that will be passed directly to the base
class' __init__ function.
See paper for reference on how it can be
used in MinDiff.