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15-eco.Rmd

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@@ -444,7 +444,7 @@ Therefore, we tune the hyperparameters\index{hyperparameter} for a good spatial
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<!-- If we used more than one repetition (say 2) we would retrieve multiple optimal tuned hyperparameter combinations (say 2) -->
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Having already constructed the input variables (`rp`), we are all set for specifying the **mlr3**\index{mlr3 (package)} building blocks (task, learner, and resampling).
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For specifying a spatial task, we use again the **mlr3spatiotempcv** package [@schratz_mlr3spatiotempcv_2021 Section \@ref(spatial-cv-with-mlr3)], and since our response (`sc`) is numeric, we use a regression\index{regression} task.
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For specifying a spatial task, we use again the **mlr3spatiotempcv** package [@schratz_mlr3spatiotempcv_2021, Section \@ref(spatial-cv-with-mlr3)], and since our response (`sc`) is numeric, we use a regression\index{regression} task.
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```{r 15-eco-20}
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# create task

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