[FIXED] Tuning of hyperparameters of SVR

Issue

What is the better way of selecting the hyperparameters of SVR for tuning them using GridSearchCV? I learnt that the input to GridSearchCV is set of values for C, gamma and epsilon. The GridSearchCV algorithm evaluates each of these values and suggests the best among the set of values given to it as input. How to select the set of values to be given as input? Is there a better way of selecting them apart from trial and error? Resorting to trial and error is time consuming and also one might miss out optimal values of the Hyperparameters.

Solution

As always, good hyperparameters range depends on the problem. It is difficult to find one solution that fit all problems.

The literature recommends an epsilon between 1-e3 and 1. Concerning the C parameter a good hyperparameter space would be between 1 and 100. A C that is too large will simply overfit the training data.

The gamma is already calculated by scikit-learn SVR. I would not change it.

Don't forget that you can also tune the kernel and this might be the most important hyperparameter to tune.

To conclude, there is no free lunch when searching for the best hyperparameter ranges. The best option is to read literature and documenation as well as understanding each parameter impact.

Answered By - Antoine Dubuis