I try to fit a data matrix X to an output vector y with a regression model in sklearn. I have some training data and some test data, where the score is the RMSE.So my best score I achieved with SVR, kernel 'poly' and tuning the hyperparameters 'C', 'degree' and 'gamma' with optunity and cross-validation.I actually don't know how to achieve better scores so I ask here in this forum for another approach. I tried already KernelRidge, Linear Regression, SVR with other kernels, Neural Networks but all of them gave worse results. It is actually possible to do better, since other people do better in this task, but I have no more Idea what I can do to imporve the score. Any Ideas?
Sklearn regression problem
scikit learn