...

book/_quarto.yml

@@ -22,33 +22,9 @@ book:
     background: "#f8f9fa"
   chapters:
     - index.qmd
-    - chapters/chapter1/introduction_and_overview.qmd
-    - part: "Fundamentals"
-      chapters:
-      - chapters/chapter2/data_and_basic_modeling.qmd
-      - chapters/chapter3/evaluation_and_benchmarking.qmd
-    - part: "Tuning and Feature Selection"
-      chapters:
-      - chapters/chapter4/hyperparameter_optimization.qmd
-      - chapters/chapter5/advanced_tuning_methods_and_black_box_optimization.qmd
-      - chapters/chapter6/feature_selection.qmd
-    - part: "Pipelines and Preprocessing"
-      chapters:
-      - chapters/chapter7/sequential_pipelines.qmd
-      - chapters/chapter8/non-sequential_pipelines_and_tuning.qmd
-      - chapters/chapter9/preprocessing.qmd
     - part: "Advanced Topics"
       chapters:
-      - chapters/chapter10/advanced_technical_aspects_of_mlr3.qmd
       - chapters/chapter11/large-scale_benchmarking.qmd
-      - chapters/chapter12/model_interpretation.qmd
-      - chapters/chapter13/beyond_regression_and_classification.qmd
-      - chapters/chapter14/algorithmic_fairness.qmd
-    - chapters/references.qmd
-  appendices:
-      - chapters/appendices/solutions.qmd # online only
-      - chapters/appendices/tasks.qmd # online only
-      - chapters/appendices/overview-tables.qmd # online only
 
 
 bibliography: book.bib

book/chapters/chapter11/large-scale_benchmarking.qmd

@@ -8,7 +8,7 @@
 ```{r large_benchmarking-001}
 #| include: false
 #| cache: false
-lgr::get_logger("mlr3oml")$set_threshold("off")
+lgr::get_logger("mlr3oml")$set_threshold("info")
 library(mlr3batchmark)
 library(batchtools)
 library(mlr3oml)
@@ -18,6 +18,11 @@ if (!dir.exists(file.path("openml", "manual"))) {
 options(mlr3oml.cache = file.path("openml", "cache"))
 ```
 
+```{r}
+getwd()
+```
+
+
 In machine learning, it is often difficult to evaluate methods using mathematical analysis alone.
 Even when formal analyses can be successfully applied, it is often an open question whether real-world datasets satisfy the necessary assumptions for the theorems to hold.
 Empirical `r index('benchmark experiments')` evaluate the performance of different algorithms on a wide range of datasets.