Just want to first of thank you for the awesome package, its being helpful in tuning models already (xgboost and randomforests).
Being able to add some custom init parameters would be awesome as a way of better including some domain knowledge. E.g. When tuning random forests I would try limiting the tree depth to 5,10 and n_features, while also limiting the mtry parameter to sqrt(n_features), n_features/3 and n_features. Running a quick grid search first and then GPGO to fine tune would be the ideal workflow.
Hi,
Just want to first of thank you for the awesome package, its being helpful in tuning models already (xgboost and randomforests).
Being able to add some custom init parameters would be awesome as a way of better including some domain knowledge. E.g. When tuning random forests I would try limiting the tree depth to 5,10 and n_features, while also limiting the mtry parameter to sqrt(n_features), n_features/3 and n_features. Running a quick grid search first and then GPGO to fine tune would be the ideal workflow.
For anyone else reading this there is a gist by @hawk31 showing how to do this manually: https://gist.github.com/hawk31/ed222c4cf6b21cbd7d4b5186f3f132b5
Thanks!