diff --git a/book/articles/machine_learning.qmd b/book/articles/machine_learning.qmd
index 0e2b83474..7fa76b926 100644
--- a/book/articles/machine_learning.qmd
+++ b/book/articles/machine_learning.qmd
@@ -49,63 +49,7 @@ avg_predictions(mod, type = "numeric", newdata = penguins, by = "island")
 When the underlying engine that `tidymodels` uses to fit the model is not supported by `marginaleffects` as a standalone model, we can also obtain correct results, but no uncertainy estimates. Here is a random forest model:
 
 ```{r}
-mod <- rand_forest(mode = "regression") |>
-    set_engine("ranger") |>
-    fit(bill_length_mm ~ ., data = penguins)
-
-avg_comparisons(mod, newdata = penguins, type = "numeric")
-```
-
-### Workflows
-
-`tidymodels` "workflows" are a convenient way to train a model while applying a series of pre-processing steps to the data. `marginaleffects` supports workflows out of the box. First, let's consider a simple regression task:
-
-```{r}
-penguins <- modeldata::penguins |> 
-  na.omit() |>
-  select(sex, island, species, bill_length_mm)
-
-mod <- penguins |>
-    recipe(bill_length_mm ~ island + species + sex, data = _) |>
-    step_dummy(all_nominal_predictors()) |>
-    workflow(spec = linear_reg(mode = "regression", engine = "glm")) |>
-    fit(penguins)
-
-avg_comparisons(mod, newdata = penguins, type = "numeric")
-```
-
-Now, we run a classification task instead, and plot the predicted probabilities:
-
-```{r}
-mod <- penguins |>
-    recipe(sex ~ island + species + bill_length_mm, data = _) |>
-    step_dummy(all_nominal_predictors()) |>
-    workflow(spec = logistic_reg(mode = "classification", engine = "glm")) |>
-    fit(penguins)
-
-plot_predictions(
-  mod,
-  condition = c("bill_length_mm", "group"),
-  newdata = penguins,
-  type = "prob")
-```
-
-Finally, let's consider a more complex task, where we train several models and summarize them in a table using `modelsummary`:
-
-```{r, error=T}
-library(modelsummary)
-
-# pre-processing
-pre <- penguins |>
-    recipe(sex ~ ., data = _) |>
-    step_ns(bill_length_mm, deg_free = 4) |>
-    step_dummy(all_nominal_predictors())
-
-# modelling strategies
-models <- list(
-  "Logit" = logistic_reg(mode = "classification", engine = "glm"),
-  "Random Forest" = rand_forest(mode = "classification", engine = "ranger"),
-  "XGBooost" = boost_tree(mode = "classification", engine = "xgboost")
+mod <- rand_forest(mode = "regression") |oost" = boost_tree(mode = "classification", engine = "xgboost")
 )
 
 # fit to data
@@ -232,7 +176,7 @@ Of course, we can customize the plot using all the standard `ggplot2` functions:
 plot_predictions(forest, by = "temp", newdata = d) +
     geom_point(data = bikes, aes(x = temp, y = count), alpha = 0.1) +
     geom_smooth(data = bikes, aes(x = temp, y = count), se = FALSE, color = "orange") +
-    labs(x = "Temperature (Celcius)", y = "Predicted number of bikes rented per hour",
-         title = "Black: random forest predictions. Green: LOESS smoother.") +
+    labs(x = "Temperature (Celsius)", y = "Predicted number of bikes rented per hour",
+         title = "Black: random forest predictions. Orange: LOESS smoother.") +
     theme_bw()
 ```