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mlr-org · Dec 26, 2024 · c5beff3 · c5beff3
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diff --git a/README.Rmd b/README.Rmd
@@ -96,6 +96,8 @@ rr = resample(new_task, ff, resampling)
 rr$aggregate(measure)
 ```
 
+### WIP
+
 ```{r, eval = FALSE}
 # doesn't work since the graph learner does its own thing
 glrn = as_learner(pop %>>% ff)$train(task)

diff --git a/README.md b/README.md
@@ -44,31 +44,31 @@ prediction = ff$predict_newdata(newdata, task)
 prediction
 #> <PredictionRegr> for 3 observations:
 #>  row_ids truth response
-#>        1    NA 450.8260
-#>        2    NA 468.3018
-#>        3    NA 443.5910
+#>        1    NA 449.4614
+#>        2    NA 474.3040
+#>        3    NA 485.6794
 prediction = ff$predict(task, 142:144)
 prediction
 #> <PredictionRegr> for 3 observations:
 #>  row_ids truth response
-#>        1   461 462.1091
-#>        2   390 412.6569
-#>        3   432 387.0121
+#>        1   461 453.5201
+#>        2   390 412.6527
+#>        3   432 398.0713
 prediction$score(measure)
 #> regr.rmse 
-#>  29.08878
+#>  23.94613
 
 resampling = rsmp("forecast_holdout", ratio = 0.8)
 rr = resample(task, ff, resampling)
 rr$aggregate(measure)
 #> regr.rmse 
-#>  106.1036
+#>  110.7019
 
 resampling = rsmp("forecast_cv")
 rr = resample(task, ff, resampling)
 rr$aggregate(measure)
 #> regr.rmse 
-#>  52.05099
+#>  52.52531
 ```
 
 ### Multivariate
@@ -87,41 +87,43 @@ prediction = ff$predict(new_task, 142:144)
 ff$predict(new_task, 142:144)
 #> <PredictionRegr> for 3 observations:
 #>  row_ids truth response
-#>        1   461 450.8613
-#>        2   390 408.7569
-#>        3   432 405.0463
+#>        1   461 448.1974
+#>        2   390 406.6182
+#>        3   432 405.1209
 prediction$score(measure)
 #> regr.rmse 
-#>  19.84202
+#>   19.6855
 
 row_ids = new_task$nrow - 0:2
 ff$predict_newdata(new_task$data(rows = row_ids), new_task)
 #> <PredictionRegr> for 3 observations:
 #>  row_ids truth response
-#>        1   432 408.5391
-#>        2   390 388.5220
-#>        3   461 391.3046
+#>        1   432 398.9129
+#>        2   390 390.0026
+#>        3   461 379.8090
 newdata = new_task$data(rows = row_ids, cols = new_task$feature_names)
 ff$predict_newdata(newdata, new_task)
 #> <PredictionRegr> for 3 observations:
 #>  row_ids truth response
-#>        1    NA 408.5391
-#>        2    NA 388.5220
-#>        3    NA 391.3046
+#>        1    NA 398.9129
+#>        2    NA 390.0026
+#>        3    NA 379.8090
 
 resampling = rsmp("forecast_holdout", ratio = 0.8)
 rr = resample(new_task, ff, resampling)
 rr$aggregate(measure)
 #> regr.rmse 
-#>  80.40145
+#>  81.72021
 
 resampling = rsmp("forecast_cv")
 rr = resample(new_task, ff, resampling)
 rr$aggregate(measure)
 #> regr.rmse 
-#>  43.81449
+#>  42.64055
 ```
 
+### WIP
+
 ``` r
 # doesn't work since the graph learner does its own thing
 glrn = as_learner(pop %>>% ff)$train(task)