I am doing load forecasting using SVR(kernel='rbf').How can I understand which is the best value for parameters C, epsilon and gamma?Thanks.
Support vector regression and paremeters
python;svm;forecast
It looks like that you are using scikit-learn. In this case use Grid Search Cross Validation  or Randomized Search Cross Validation to find the best parameters. sklearn.grid_search.GridSearchCV(estimator, param_grid, scoring=None, cv=None, ...)In these approaches you basically loop over possible sets of your parameters, specified via param_grid. For each set you perform a cross-validation (default is 3-fold, but you can specify it via cv parameter). Cross-validation gives you the mean value and deviation of your 'scoring' parameter (e.g. 'accuracy' for classification, 'r2' for regression). The set of parameters with the best 'scoring' is the winner. See example here. It also show how to output not only the best set of parameters, but also top-N. I find it very useful in building intuition about my model. 