{"action":"created","issue":{"url":"https://api.github.com/repos/berndbischl/mlr/issues/223","labels_url":"https://api.github.com/repos/berndbischl/mlr/issues/223/labels{/name}","comments_url":"https://api.github.com/repos/berndbischl/mlr/issues/223/comments","events_url":"https://api.github.com/repos/berndbischl/mlr/issues/223/events","html_url":"https://github.com/berndbischl/mlr/issues/223","id":56065583,"number":223,"title":"Use sub-model trick for hyperparameter tuning","user":{"login":"zachmayer","id":581590,"avatar_url":"https://avatars.githubusercontent.com/u/581590?v=3","gravatar_id":"","url":"https://api.github.com/users/zachmayer","html_url":"https://github.com/zachmayer","followers_url":"https://api.github.com/users/zachmayer/followers","following_url":"https://api.github.com/users/zachmayer/following{/other_user}","gists_url":"https://api.github.com/users/zachmayer/gists{/gist_id}","starred_url":"https://api.github.com/users/zachmayer/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/zachmayer/subscriptions","organizations_url":"https://api.github.com/users/zachmayer/orgs","repos_url":"https://api.github.com/users/zachmayer/repos","events_url":"https://api.github.com/users/zachmayer/events{/privacy}","received_events_url":"https://api.github.com/users/zachmayer/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"milestone":null,"comments":1,"created_at":"2015-01-30T18:11:30Z","updated_at":"2015-01-30T18:15:37Z","closed_at":null,"body":"Many models actually fit multiple models in one \"pass\".  For example, a `gbm` with 1,000 trees can be used to make predictions for 1-999 trees and a `glmnet` model can be used to predict for many different lambda values along the regularization path.\r\n\r\nThe various methods for parameter turning (e.g. grid search and irace) should be aware of this.\r\n\r\nFor example, with a gbm, you only need to fit each combination of distribution, interaction.depth, n.minobsinnode, shrinkage, bag.fraction and train.fraction with the maximum number of trees, and you get predictions for each lesser value of `n.trees` for free."},"comment":{"url":"https://api.github.com/repos/berndbischl/mlr/issues/comments/72244861","html_url":"https://github.com/berndbischl/mlr/issues/223#issuecomment-72244861","issue_url":"https://api.github.com/repos/berndbischl/mlr/issues/223","id":72244861,"user":{"login":"berndbischl","id":1225974,"avatar_url":"https://avatars.githubusercontent.com/u/1225974?v=3","gravatar_id":"","url":"https://api.github.com/users/berndbischl","html_url":"https://github.com/berndbischl","followers_url":"https://api.github.com/users/berndbischl/followers","following_url":"https://api.github.com/users/berndbischl/following{/other_user}","gists_url":"https://api.github.com/users/berndbischl/gists{/gist_id}","starred_url":"https://api.github.com/users/berndbischl/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/berndbischl/subscriptions","organizations_url":"https://api.github.com/users/berndbischl/orgs","repos_url":"https://api.github.com/users/berndbischl/repos","events_url":"https://api.github.com/users/berndbischl/events{/privacy}","received_events_url":"https://api.github.com/users/berndbischl/received_events","type":"User","site_admin":false},"created_at":"2015-01-30T18:15:37Z","updated_at":"2015-01-30T18:15:37Z","body":"Yes, I thought about this many time, you are perfectly right.\r\n\r\nI am unsure whether it is \"trivial\" to make that work. But we can of course try.\r\nBefore, we have more a less favored \"generality\" instead of exploiting such specific extra tricks.\r\n\r\nBut I guess we are now very general and can start with the fiddling around for the special cases ;-)\r\n\r\nI need to warn you though, that we have some other issues before that, that are more important than this. So I am unsure how long solving this here will take.\r\n\r\nCan I also ask - just out of curiosity - what you are using the package for? \r\n\r\n"}}