fix broken links

Author: jannes-m

12-spatial-cv.Rmd

@@ -279,7 +279,7 @@ Third, the **resampling** approach assesses the predictive performance of the mo
 ### Generalized linear model {#glm}
 
 To implement a GLM\index{GLM} in **mlr3**\index{mlr3 (package)}, we must create a **task** containing the landslide data.
-Since the response is binary (two-category variable) and has a spatial dimension, we create a classification\index{classification} task with `TaskClassifST$new()` of the **mlr3spatiotempcv** package [@schratz_mlr3spatiotempcv_2021, for non-spatial tasks, use `mlr3::TaskClassif$new()` or `mlr3::TaskRegr$new()` for regression\index{regression} tasks, see `?Task` for other task types].^[The **mlr3** ecosystem makes heavily use of **data.table** and **R6** classes. And though you might use **mlr3** without knowing the specifics of **data.table** or **R6**, it might be rather helpful. To learn more about **data.table**, please refer to https://rdatatable.gitlab.io/data.table/index.html. To learn more about **R6**, we recommend [Chapter 14](https://adv-r.hadley.nz/fp.html) of the Advanced R book [@wickham_advanced_2019].]
+Since the response is binary (two-category variable) and has a spatial dimension, we create a classification\index{classification} task with `TaskClassifST$new()` of the **mlr3spatiotempcv** package [@schratz_mlr3spatiotempcv_2021, for non-spatial tasks, use `mlr3::TaskClassif$new()` or `mlr3::TaskRegr$new()` for regression\index{regression} tasks, see `?Task` for other task types].^[The **mlr3** ecosystem makes heavily use of **data.table** and **R6** classes. And though you might use **mlr3** without knowing the specifics of **data.table** or **R6**, it might be rather helpful. To learn more about **data.table**, please refer to https://rdatatable.gitlab.io/data.table/. To learn more about **R6**, we recommend [Chapter 14](https://adv-r.hadley.nz/fp.html) of the Advanced R book [@wickham_advanced_2019].]
 The first essential argument of these `Task*$new()` functions is `backend`.
 `backend` expects that the input data includes the response and predictor variables.
 The `target` argument indicates the name of a response variable (in our case this is `lslpts`) and `positive` determines which of the two factor levels of the response variable indicate the landslide initiation point (in our case this is `TRUE`).
@@ -466,7 +466,7 @@ For those wishing to apply a random forest model, we recommend to read this chap
 
 SVMs\index{SVM} search for the best possible 'hyperplanes' to separate classes (in a classification\index{classification} case) and estimate 'kernels' with specific hyperparameters\index{hyperparameter} to create non-linear boundaries between classes [@james_introduction_2013].
 Machine learning algorithms often feature hyperparameters\index{hyperparameter} and parameters.
-Parameters can be estimated from the data while hyperparameters\index{hyperparameter} are set before the learning begins (see also the [machine mastery blog](https://machinelearningmastery.com/difference-between-a-parameter-and-a-hyperparameter/) and the [hyperparameter optimization chapter](https://mlr3book.mlr-org.com/optimization.html) of the mlr3 book).
+Parameters can be estimated from the data while hyperparameters\index{hyperparameter} are set before the learning begins (see also the [machine mastery blog](https://machinelearningmastery.com/difference-between-a-parameter-and-a-hyperparameter/) and the [hyperparameter optimization chapter](https://mlr3book.mlr-org.com/chapters/chapter4/hyperparameter_optimization.html) of the mlr3 book).
 The optimal hyperparameter\index{hyperparameter} configuration is usually found within a specific search space and determined with the help of cross-validation methods.
 This is called hyperparameter\index{hyperparameter} tuning and the main topic of this section.
 
@@ -486,8 +486,7 @@ mlr3_learners[class == "classif" & grepl("svm", id),
 Of the options, we will use `ksvm()` from the **kernlab** package [@karatzoglou_kernlab_2004].
 To allow for non-linear relationships, we use the popular radial basis function (or Gaussian) kernel (`"rbfdot" `) which is also the default of `ksvm()`.
 Setting the`type` argument to `"C-svc"` makes sure that `ksvm()` is solving a classification task. 
-To make sure that the tuning does not stop because of one failing model, we additionally define a fallback learner (for more information please refer to https://mlr3book.mlr-org.com/technical.html#fallback-learners).
-
+To make sure that the tuning does not stop because of one failing model, we additionally define a fallback learner (for more information please refer to https://mlr3book.mlr-org.com/chapters/chapter10/advanced_technical_aspects_of_mlr3.html#sec-fallback).
 ```{r 12-spatial-cv-24}
 lrn_ksvm = mlr3::lrn("classif.ksvm", predict_type = "prob", kernel = "rbfdot",
                      type = "C-svc")
@@ -623,7 +622,7 @@ round(mean(score_spcv_svm$classif.auc), 2)
 
 It appears that the GLM\index{GLM} (aggregated AUROC\index{AUROC} was `r score[resampling_id == "repeated_spcv_coords" & learner_id == "classif.log_reg", round(mean(classif.auc), 2)]`) is slightly better than the SVM\index{SVM} in this specific case.
 To guarantee an absolute fair comparison, one should also make sure that the two models use the exact same partitions -- something we have not shown here but have silently used in the background (see `code/12_cv.R` in the book's github repo for more information).
-To do so, **mlr3** offers the functions `benchmark_grid()` and `benchmark()` [see also https://mlr3book.mlr-org.com/perf-eval-cmp.html#benchmarking, @becker_mlr3_2022]. 
+To do so, **mlr3** offers the functions `benchmark_grid()` and `benchmark()` [see also https://mlr3book.mlr-org.com/chapters/chapter3/evaluation_and_benchmarking.html#sec-benchmarking, @becker_mlr3_2022]. 
 We will explore these functions in more detail in the Exercises.
 Please note also that using more than 50 iterations in the random search of the SVM would probably yield hyperparameters\index{hyperparameter} that result in models with a better AUROC [@schratz_hyperparameter_2019].
 On the other hand, increasing the number of random search iterations would also increase the total number of models and thus runtime.
@@ -646,7 +645,7 @@ Machine learning algorithms often require hyperparameter\index{hyperparameter} i
 Machine learning overall, and its use to understand spatial data, is a large field and this chapter has provided the basics, but there is more to learn.
 We recommend the following resources in this direction:
 
-- The **mlr3 book** [@becker_mlr3_2022; https://mlr-org.github.io/mlr-tutorial/release/html/] and especially the [chapter on the handling of spatio-temporal data](https://mlr3book.mlr-org.com/spatiotemporal.html)
+- The **mlr3 book** [@becker_mlr3_2022; https://mlr3book.mlr-org.com/] and especially the [chapter on the handling of spatio-temporal data](https://mlr3book.mlr-org.com/chapters/chapter13/beyond_regression_and_classification.html#sec-spatiotemporal)
 - An academic paper on hyperparameter\index{hyperparameter} tuning [@schratz_hyperparameter_2019]
 - An academic paper on how to use **mlr3spatiotempcv** [@schratz_mlr3spatiotempcv_2021]
 - In case of spatio-temporal data, one should account for spatial\index{autocorrelation!spatial} and temporal\index{autocorrelation!temporal} autocorrelation when doing CV\index{cross-validation} [@meyer_improving_2018]