docs: readme

README.Rmd

@@ -30,6 +30,7 @@ Extending mlr3 to time series forecasting.
 ```{=gfm}
 > [!IMPORTANT]
 > This package is in an early stage of development and should be considered experimental.
+> If you are interested in experimenting with it, we welcome your feedback!
 ```
 
 ## Installation

README.md

@@ -17,6 +17,7 @@ status](https://www.r-pkg.org/badges/version/mlr3forecast)](https://CRAN.R-proje
 
 > [!IMPORTANT]
 > This package is in an early stage of development and should be considered experimental.
+> If you are interested in experimenting with it, we welcome your feedback!
 
 ## Installation
 
@@ -44,32 +45,32 @@ prediction = ff$predict_newdata(newdata, task)
 prediction
 #> <PredictionRegr> for 3 observations:
 #>  row_ids truth response
-#>        1    NA 444.7671
-#>        2    NA 473.9790
-#>        3    NA 480.9669
+#>        1    NA 445.1830
+#>        2    NA 478.2606
+#>        3    NA 484.7871
 prediction = ff$predict(task, 142:144)
 prediction
 #> <PredictionRegr> for 3 observations:
 #>  row_ids truth response
-#>        1   461 462.5280
-#>        2   390 410.8195
-#>        3   432 388.3864
+#>        1   461 458.0830
+#>        2   390 412.4142
+#>        3   432 398.7917
 prediction$score(measure)
 #> regr.rmse 
-#>  27.91612
+#>  23.19263
 
 ff = Forecaster$new(lrn("regr.ranger"), 1:3)
 resampling = rsmp("forecast_holdout", ratio = 0.8)
 rr = resample(task, ff, resampling)
 rr$aggregate(measure)
 #> regr.rmse 
-#>  115.1751
+#>  113.1917
 
 resampling = rsmp("forecast_cv")
 rr = resample(task, ff, resampling)
 rr$aggregate(measure)
 #> regr.rmse 
-#>  51.15563
+#>  50.52313
 ```
 
 ### Multivariate
@@ -88,39 +89,39 @@ prediction = ff$predict(new_task, 142:144)
 ff$predict(new_task, 142:144)
 #> <PredictionRegr> for 3 observations:
 #>  row_ids truth response
-#>        1   461 450.8882
-#>        2   390 405.8591
-#>        3   432 405.1108
+#>        1   461 454.2787
+#>        2   390 402.9731
+#>        3   432 404.8773
 prediction$score(measure)
 #> regr.rmse 
-#>  18.94544
+#>  17.78686
 
 row_ids = new_task$nrow - 0:2
 ff$predict_newdata(new_task$data(rows = row_ids), new_task)
 #> <PredictionRegr> for 3 observations:
 #>  row_ids truth response
-#>        1   432 407.3052
-#>        2   390 386.6901
-#>        3   461 390.5332
+#>        1   432 402.6563
+#>        2   390 389.9071
+#>        3   461 384.1679
 newdata = new_task$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 407.3052
-#>        2    NA 386.6901
-#>        3    NA 390.5332
+#>        1    NA 402.6563
+#>        2    NA 389.9071
+#>        3    NA 384.1679
 
 resampling = rsmp("forecast_holdout", ratio = 0.8)
 rr = resample(new_task, ff, resampling)
 rr$aggregate(measure)
 #> regr.rmse 
-#>  80.58328
+#>  84.17676
 
 resampling = rsmp("forecast_cv")
 rr = resample(new_task, ff, resampling)
 rr$aggregate(measure)
 #> regr.rmse 
-#>  44.15569
+#>  45.40293
 ```
 
 ### mlr3pipelines integration
@@ -130,5 +131,5 @@ glrn = as_learner(pop %>>% ff)$train(task)
 prediction = glrn$predict(task, 142:144)
 prediction$score(measure)
 #> regr.rmse 
-#>  19.22717
+#>  19.75408
 ```