The website is in Maintenance mode. We are in the process of adding more features.
Any new bookmarks, comments, or user profiles made during this time will not be saved.

AIML.com

Machine Learning Resources

How are model hyper-parameters generally selected?

Bookmark this question

Model hyper-parameters are usually chosen through a grid search procedure, in which a model is trained separately on every combination of hyper-parameters in the grid, and an error metric is stored after the model is evaluated on each setting combination. The setting that results in the lowest cross-validation error is usually considered the optimal combination. It is important to search enough combinations of the grid of hyperparameters to be able to see which settings are resulting in both under and overfitting, and that the optimal setting for each hyperparameter can be found within the range searched. 

Leave your Comments and Suggestions below:

Please Login or Sign Up to leave a comment

Partner Ad  

Find out all the ways
that you can

Explore Questions by Topics