kou | A minimal language compiled into wasm bytecode

I'm trying to use a MLPregressor from scikit learn in order to do a non linear regression on a set of 260 examples (X,Y). One example is composed of 200 features for X and 1 feature for Y.

File containing X

File containing Y

The link between X and Y is not obvious if directly plotted together but if we plot x=log10(sum(X)) and y=log10(Y), the link between both is almost linear. As a first approach, I tried to apply my neural network directly on X and Y without success.

I have read that scaling would improve regression. In my case, Y is containing datas in a very wide range of values (from 10e-12 to 10e-5). When computing the error, of course 10e-5 as much more weight than 10e-12. But I would like my neural network to correctly approximate both. When using a linear scaling, let's say preprocessing.MinMaxScaler from scikit learn, 10e-8 ~ -0.99 and 10e-12 ~ -1. So I'm loosing all the information of my target.

My question here is: what kind of scaling could I use to get consistent results? The only solution I have found is to apply log10(Y) but of course, error is increased exponentially.

The best I could get is with the code below: