eval=FALSE in c15

15-eco.Rmd

@@ -505,30 +505,35 @@ autotuner_rf = mlr3tuning::auto_tuner(
 
 Calling the `train()`-method of the `AutoTuner`-object finally runs the hyperparameter\index{hyperparameter} tuning, and will find the optimal hyperparameter\index{hyperparameter} combination for the specified parameters.
 
-```{r 15-eco-24, eval=TRUE, cache=TRUE, cache.lazy=FALSE}
+```{r 15-eco-24, eval=FALSE, cache=TRUE, cache.lazy=FALSE}
 # hyperparameter tuning
 set.seed(08012024)
-autotuner_rf$train(task, store_backends = TRUE)
+autotuner_rf$train(task)
 ```
 
-```{r 15-eco-25, eval=TRUE, echo=FALSE}
+```{r 15-eco-25, cache=TRUE, cache.lazy=FALSE, eval=FALSE, echo=FALSE}
 saveRDS(autotuner_rf, "extdata/15-tune.rds")
 ```
 
-```{r 15-eco-26, echo=FALSE}
+```{r 15-eco-26, echo=FALSE, eval=FALSE}
 autotuner_rf = readRDS("extdata/15-tune.rds")
 ```
 
-```{r tuning-result, cache=TRUE, cache.lazy=FALSE}
+<!-- TODO: evaluate this when issue fixed upstream -->
+
+```{r tuning-result, eval=FALSE}
 autotuner_rf$tuning_result
+#>     mtry sample.fraction min.node.size learner_param_vals  x_domain regr.rmse
+#>    <int>           <num>         <int>             <list>    <list>     <num>
+#> 1:     4           0.878             7          <list[4]> <list[3]>     0.368
 ```
 
 ### Predictive mapping
 
 The tuned hyperparameters\index{hyperparameter} can now be used for the prediction.
 To do so, we only need to run the `predict` method of our fitted `AutoTuner` object.
 
-```{r 15-eco-27, cache=TRUE, cache.lazy=FALSE, warning=FALSE}
+```{r 15-eco-27, cache=TRUE, cache.lazy=FALSE, warning=FALSE, eval=FALSE}
 # predicting using the best hyperparameter combination
 autotuner_rf$predict(task)
 ```