remove warnings in preprocessing chapter (#893)

Author: be-marc

book/chapters/chapter9/preprocessing.qmd

@@ -236,17 +236,9 @@ invisible(dev.off())
 magick::image_trim(fig)
 ```
 
-::: {.callout-warning}
-
-Currently, there is a bug in the mlr3pipelines package that causes the following code chunk to fail.
-See https://github.com/mlr-org/mlr3pipelines/issues/894 for more details.
-
-:::
-
 Using this pipeline we can now run experiments with `lrn("regr.ranger")`, which cannot handle missing data; we also compare a simpler pipeline that only uses OOR imputation to demonstrate performance differences resulting from different strategies.
 
 ```{r preprocessing-015}
-#| eval: false
 glrn_rf_impute_hist = as_learner(impute_hist %>>% lrn("regr.ranger"))
 glrn_rf_impute_hist$id = "RF_imp_Hist"
 
@@ -450,19 +442,10 @@ tsk_ames_ext$data(1,
   c("energy_means", "energy_mins", "energy_maxs", "energy_vars"))
 ```
 
-::: {.callout-warning}
-
-This code chunk does not work due to the bug in the `mlr3pipelines` package.
-See the warning message above for more details.
-
-:::
-
-
 These outputs look sensible compared to @fig-energy so we can now run our final benchmark experiment using feature extraction.
 We do not need to add the `PipeOp` to each learner as we can apply it once (as above) before any model training by applying it to all available data.
 
 ```{r preprocessing-026, warning=FALSE, R.options = list(datatable.print.nrows = 13, datatable.print.class = FALSE, datatable.print.keys = FALSE, datatable.print.trunc.cols = TRUE)}
-#| eval: false
 learners = list(lrn_baseline, lrn("regr.rpart"), glrn_xgb_impact,
     glrn_rf_impute_oor, glrn_lm_robust, glrn_log_lm_robust)