Sebastian Fischer
Sebastian Fischer
```r #' @title Custom Function #' @inherit torch::nnf_linear description #' @section nn_module: #' Calls [`torch::nn_linear()`] when trained where the parameter `in_features` is inferred as the second #' to last dimension...
So the problem is that `.Platform$pkgType` shows `"source"`
Thanks for the quick response! :) What do you think abou trunning the installation instructions in the CI to ensure that they are working?
my previous answers were bad and thanks for making me aware that this is not documented yet. ``` r library(mlr3) library(mlr3tuning) #> Loading required package: paradox library(mlr3pipelines) library(mlr3proba) library(mlr3extralearners) task...
Maybe we should include the internal tune tokens in the tuning spaces @be-marc?
you are accessing the final model fit but in the final model fit there is no early stopping. This is, because during the final model fit we want to use...
Also, are you aware that xgboost will use the optimal model during prediction and NOT the final model? --> You should be less worried about a too high patience parameter...
Looking more into this, I am not sure whether we handle stratification and grouping perfectly. In the code snippets below, I created to constructed tasks with specific grouping and stratification...
Also see here: https://github.com/davidtvs/pytorch-lr-finder The interface should just be a simple function I guess.