Fix doc pages for prediction type bug an unexpected problem or unintended behavior documentation

man/rmd/bag_tree_C5.0.Rmd

@@ -61,7 +61,7 @@ bag_tree(min_n = integer()) |>
 ```{r}
 #| label: predict-types
 
-parsnip:::get_from_env("bag_mars_predict") |>
+parsnip:::get_from_env("bag_tree_predict") |>
   dplyr::filter(engine == "C5.0") |>
   dplyr::select(mode, type)
 
@@ -72,4 +72,3 @@ parsnip:::get_from_env("bag_mars_predict") |>
  - Breiman, L. 1996. "Bagging predictors". Machine Learning. 24 (2): 123-140
 
  - Kuhn, M, and K Johnson. 2013. *Applied Predictive Modeling*. Springer.
-

man/rmd/bag_tree_C5.0.md

@@ -55,19 +55,21 @@ The `fit()` and `fit_xy()` functions have arguments called `case_weights` that e
 
 
 ``` r
-parsnip:::get_from_env("bag_mars_predict") |>
+parsnip:::get_from_env("bag_tree_predict") |>
   dplyr::filter(engine == "C5.0") |>
   dplyr::select(mode, type)
 ```
 
 ```
-## # A tibble: 0 x 2
-## # i 2 variables: mode <chr>, type <chr>
+## # A tibble: 2 x 2
+##   mode           type 
+##   <chr>          <chr>
+## 1 classification class
+## 2 classification prob
 ```
 
 ## References
 
  - Breiman, L. 1996. "Bagging predictors". Machine Learning. 24 (2): 123-140
 
  - Kuhn, M, and K Johnson. 2013. *Applied Predictive Modeling*. Springer.
-

man/rmd/bag_tree_rpart.Rmd

@@ -115,7 +115,7 @@ bag_tree(
 
 ```{r}
 #| label: predict-types
-parsnip:::get_from_env("bag_mars_predict") |>
+parsnip:::get_from_env("bag_tree_predict") |>
   dplyr::filter(engine == "rpart") |>
   dplyr::select(mode, type)
 ```

man/rmd/bag_tree_rpart.md

@@ -147,14 +147,20 @@ The underlying model implementation does not allow for case weights.
 
 
 ``` r
-parsnip:::get_from_env("bag_mars_predict") |>
+parsnip:::get_from_env("bag_tree_predict") |>
   dplyr::filter(engine == "rpart") |>
   dplyr::select(mode, type)
 ```
 
 ```
-## # A tibble: 0 x 2
-## # i 2 variables: mode <chr>, type <chr>
+## # A tibble: 5 x 2
+##   mode                type    
+##   <chr>               <chr>   
+## 1 regression          numeric 
+## 2 classification      class   
+## 3 classification      prob    
+## 4 censored regression time    
+## 5 censored regression survival
 ```
 
 ## Other details

man/rmd/null-model.Rmd

@@ -27,6 +27,7 @@ null_model() |>
 #| label: predict-types
 
 parsnip:::get_from_env("null_model_predict") |>
+  dplyr::filter(engine == "parsnip") |>
   dplyr::select(mode, type)
 
-```
+```

man/rmd/null-model.md

@@ -44,18 +44,17 @@ null_model() |>
 
 ``` r
 parsnip:::get_from_env("null_model_predict") |>
+  dplyr::filter(engine == "parsnip") |>
   dplyr::select(mode, type)
 ```
 
 ```
-## # A tibble: 7 x 2
-##   mode                type   
-##   <chr>               <chr>  
-## 1 regression          numeric
-## 2 regression          raw    
-## 3 classification      class  
-## 4 classification      prob   
-## 5 classification      raw    
-## 6 censored regression time   
-## # i 1 more row
+## # A tibble: 5 x 2
+##   mode           type   
+##   <chr>          <chr>  
+## 1 regression     numeric
+## 2 regression     raw    
+## 3 classification class  
+## 4 classification prob   
+## 5 classification raw
 ```

man/rmd/ordinal_reg_polr.Rmd

@@ -20,6 +20,15 @@ ordinal_reg() %>%
   translate()
 ```
 
+## Prediction types
+
+```{r}
+#| label: predict-types
+parsnip:::get_from_env("ordinal_reg_predict") |>
+  dplyr::filter(engine == "polr") |>
+  dplyr::select(mode, type)
+```
+
 ## References
 
 - Bürkner P-C, Vuorre M. 2019. Ordinal Regression Models in Psychology: A Tutorial. _Advances in Methods and Practices in Psychological Science_ 2(1):77-101. \doi{10.1177/2515245918823199}.

man/rmd/ordinal_reg_polr.md

@@ -30,6 +30,23 @@ ordinal_reg() %>%
 ## MASS::polr(formula = missing_arg(), data = missing_arg(), weights = missing_arg())
 ```
 
+## Prediction types
+
+
+``` r
+parsnip:::get_from_env("ordinal_reg_predict") |>
+  dplyr::filter(engine == "polr") |>
+  dplyr::select(mode, type)
+```
+
+```
+## # A tibble: 2 x 2
+##   mode           type 
+##   <chr>          <chr>
+## 1 classification class
+## 2 classification prob
+```
+
 ## References
 
 - Bürkner P-C, Vuorre M. 2019. Ordinal Regression Models in Psychology: A Tutorial. _Advances in Methods and Practices in Psychological Science_ 2(1):77-101. \doi{10.1177/2515245918823199}.

man/rmd/rand_forest_grf.Rmd

@@ -101,7 +101,15 @@ The regression and classification models enable the use of case weights. The qua
 
 The "Fitting and Predicting with parsnip" [article](https://www.tidymodels.org/learn/models/parsnip-predictions/) contains examples for `rand_forest()` with the `"grf"` engine.
 
+## Prediction types
+
+```{r}
+#| label: predict-types
+parsnip:::get_from_env("rand_forest_predict") |>
+  dplyr::filter(engine == "grf") |>
+  dplyr::select(mode, type)
+```
+
 ## References
 
 Athey, Susan, Julie Tibshirani, and Stefan Wager. "Generalized Random Forests". _Annals of Statistics_, 47(2), 2019.
-

man/rmd/rand_forest_grf.md

@@ -141,7 +141,27 @@ The regression and classification models enable the use of case weights. The qua
 
 The "Fitting and Predicting with parsnip" [article](https://www.tidymodels.org/learn/models/parsnip-predictions/) contains examples for `rand_forest()` with the `"grf"` engine.
 
+## Prediction types
+
+
+``` r
+parsnip:::get_from_env("rand_forest_predict") |>
+  dplyr::filter(engine == "grf") |>
+  dplyr::select(mode, type)
+```
+
+```
+## # A tibble: 6 x 2
+##   mode                type    
+##   <chr>               <chr>   
+## 1 classification      class   
+## 2 classification      prob    
+## 3 classification      conf_int
+## 4 regression          numeric 
+## 5 regression          conf_int
+## 6 quantile regression quantile
+```
+
 ## References
 
 Athey, Susan, Julie Tibshirani, and Stefan Wager. "Generalized Random Forests". _Annals of Statistics_, 47(2), 2019.
-

man/rmd/rand_forest_ordinalForest.Rmd

@@ -57,6 +57,15 @@ rand_forest(
 
 By default, parallel processing is turned off. When tuning, it is more efficient to parallelize over the resamples and tuning parameters. To parallelize the construction of the trees within the model, change the `num.threads` argument via [set_engine()]. 
 
+## Prediction types
+
+```{r}
+#| label: predict-types
+parsnip:::get_from_env("rand_forest_predict") |>
+  dplyr::filter(engine == "ordinalForest") |>
+  dplyr::select(mode, type)
+```
+
 ## References
 
 - Hornung R. 2020. Ordinal Forests. _Journal of Classification_ 37:4-17. \doi{10.1007/s00357-018-9302-x}.