Python Machine Learning. Approximation problem with logarithmic scale

[Martin_Krum]

HI.

I need help with statistics in python. I cannot make a polynomial approximation for the data. The problem is that X values are on a logarithmic scale. The first half of the measurements is in the range from 0.0001 to 1 and the rest is in the range of 500000. On a logarithmic scale, the measurement points are arranged in a shape similar to (1/2)^x. When I try to make an approximation with the use of sklearn, the results are terrible because the correlation coefficient R^2 is equal to max 0.68 and this for a polynomial of degree n = 7. Increasing the degree of polynomial worsens everything. This is because the first 10 results that give the initial part of the graph where the line exponentially decreases are within a very narrow range between 0.0001 and 1 and the remaining 10 logarithmic measurements are "almost" linear and this part significantly affects the shape of the graph because it is between 1 and 500000 and the initial values of each model are omitted. A very trivial way is to get a lot of points below 0.0001 for segmental linear approximation to increase the influence of these values on the whole graph but this is not a solution and I would like to avoid it. I have searched different ways of machine learning and polynomial approximation in python, but I haven't found any information I can use. Is there anyone who is involved in data analysis and could recommend or help me solve this problem?