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geocompx · Oct 4, 2024 · 023c061 · 023c061
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diff --git a/15-eco.Rmd b/15-eco.Rmd
@@ -457,7 +457,7 @@ task = mlr3spatiotempcv::as_task_regr_st(
 
 Using an `sf` object as the backend automatically provides the geometry information needed for the spatial partitioning later on.
 Additionally, we got rid of the columns `id` and `spri`, since these variables should not be used as predictors in the modeling.
-Next, we go on to construct the a random forest\index{random forest} learner from the **ranger** package [@wright_ranger_2017].
+Next, we go on to construct a random forest\index{random forest} learner from the **ranger** package [@wright_ranger_2017].
 
 ```{r 15-eco-21}
 lrn_rf = lrn("regr.ranger", predict_type = "response")