Merge pull request #592 from tidymodels/examples-article

Update Examples article

Author: hfrick

DESCRIPTION

@@ -70,9 +70,25 @@ Suggests:
 VignetteBuilder: 
     knitr
 ByteCompile: true
-Config/Needs/website: C50, earth, glmnet, keras, kernlab, kknn, LiblineaR,
-    mgcv, nnet, parsnip, randomForest, ranger, rpart, rstanarm, tidymodels,
-    tidyverse/tidytemplate, xgboost
+Config/Needs/website: 
+    C50, 
+    dbarts, 
+    earth, 
+    glmnet, 
+    keras, 
+    kernlab, 
+    kknn, 
+    LiblineaR,
+    mgcv, 
+    nnet, 
+    parsnip, 
+    randomForest, 
+    ranger, 
+    rpart, 
+    rstanarm, 
+    tidymodels,
+    tidyverse/tidytemplate,
+    xgboost
 Encoding: UTF-8
 LazyData: true
 Roxygen: list(markdown = TRUE)

vignettes/articles/Examples.Rmd

@@ -24,6 +24,81 @@ These examples show how to *fit* and *predict* with different combinations of mo
 The following examples use consistent data sets throughout. For regression, we use the Chicago ridership data. For classification, we use an artificial data set for a binary example and the Palmer penguins data for a multiclass example.
 
 
+## `bart()` models
+
+  <details id="bart-dbarts"> 
+  
+  <summary>With the `"dbarts"` engine</summary>
+  
+  <h3>Regression Example (`dbarts`)</h3>
+
+  ```{r echo=FALSE}
+  knitr::spin_child("template-reg-chicago.R")
+  ```
+
+  We can define the model with specific parameters:
+
+  ```{r}
+  bt_reg_spec <- 
+    bart(trees = 15) %>% 
+    # This model can be used for classification or regression, so set mode
+    set_mode("regression") %>% 
+    set_engine("dbarts")
+  bt_reg_spec
+  ```
+
+  Now we create the model fit object:
+
+  ```{r}
+  set.seed(1)
+  bt_reg_fit <- bt_reg_spec %>% fit(ridership ~ ., data = Chicago_train)
+  bt_reg_fit
+  ```
+
+  The holdout data can be predicted:
+
+  ```{r}
+  predict(bt_reg_fit, Chicago_test)
+  ```
+
+
+  <h3>Classification Example (`dbarts`)</h3>
+
+  ```{r echo=FALSE}
+  knitr::spin_child("template-cls-two-class.R")
+  ```
+
+  We can define the model with specific parameters:
+
+  ```{r}
+  bt_cls_spec <- 
+    bart(trees = 15) %>% 
+    # This model can be used for classification or regression, so set mode
+    set_mode("classification") %>% 
+    set_engine("dbarts")
+  bt_cls_spec
+  ```
+
+  Now we create the model fit object:
+
+  ```{r}
+  set.seed(1)
+  bt_cls_fit <- bt_cls_spec %>% fit(Class ~ ., data = data_train)
+  bt_cls_fit
+  ```
+
+  The holdout data can be predicted for both hard class predictions and probabilities. We'll bind these together into one tibble:
+
+  ```{r}
+  bind_cols(
+    predict(bt_cls_fit, data_test),
+    predict(bt_cls_fit, data_test, type = "prob")
+  )
+  ```
+    
+  </details>
+
+
 ## `boost_tree()` models
 
   <details id="boost-tree-xgboost"> 
@@ -252,6 +327,82 @@ The following examples use consistent data sets throughout. For regression, we u
   ```
 
   </details>
+
+## `gen_additive_mod()` models
+
+  <details id="gen-additive-mod-mgcv"> 
+  
+  <summary>With the `"mgcv"` engine</summary>
+  
+  <h3>Regression Example (`mgcv`)</h3>
+
+  ```{r echo=FALSE}
+  knitr::spin_child("template-reg-chicago.R")
+  ```
+
+  We can define the model with specific parameters:
+
+  ```{r}
+  gam_reg_spec <- 
+    gen_additive_mod(select_features = FALSE, adjust_deg_free = 10) %>% 
+    # This model can be used for classification or regression, so set mode
+    set_mode("regression") %>% 
+    set_engine("mgcv")
+  gam_reg_spec
+  ```
+
+  Now we create the model fit object:
+
+  ```{r}
+  set.seed(1)
+  gam_reg_fit <- gam_reg_spec %>% 
+    fit(ridership ~ Clark_Lake + Quincy_Wells, data = Chicago_train)
+  gam_reg_fit
+  ```
+
+  The holdout data can be predicted:
+
+  ```{r}
+  predict(gam_reg_fit, Chicago_test)
+  ```
+
+
+  <h3>Classification Example (`mgcv`)</h3>
+
+  ```{r echo=FALSE}
+  knitr::spin_child("template-cls-two-class.R")
+  ```
+
+  We can define the model with specific parameters:
+
+  ```{r}
+  gam_cls_spec <- 
+    gen_additive_mod(select_features = FALSE, adjust_deg_free = 10) %>% 
+    # This model can be used for classification or regression, so set mode
+    set_mode("classification") %>% 
+    set_engine("mgcv")
+  gam_cls_spec
+  ```
+
+  Now we create the model fit object:
+
+  ```{r}
+  set.seed(1)
+  gam_cls_fit <- gam_cls_spec %>% fit(Class ~ A + B, data = data_train)
+  gam_cls_fit
+  ```
+
+  The holdout data can be predicted for both hard class predictions and probabilities. We'll bind these together into one tibble:
+
+  ```{r}
+  bind_cols(
+    predict(gam_cls_fit, data_test),
+    predict(gam_cls_fit, data_test, type = "prob")
+  )
+  ```
+    
+  </details>
+
 
 ## `linear_reg()` models
 

vignettes/articles/template-cls-two-class.R

@@ -1,4 +1,4 @@
-#' The example data has two predictors and an outcome with two classes. Both predictors are in the same units
+#' The example data has two predictors and an outcome with two classes. Both predictors are in the same units.
 
 #+ results = "hide", messages = FALSE
 library(tidymodels)