📚 Added function introduction

* The classification section has a primer of factors
* The regression section is empty...

docs/classification_functions.qmd

@@ -1,3 +1,48 @@
+---
+format:
+  html:
+    code-overflow: wrap
+execute: 
+  cache: true
+knitr:
+  opts_chunk:
+    comment: "#>"
+    messages: true
+    warning: false
+---
+
 # Classification functions {.unnumbered}
 
-In this section all classification evaluation metrics are listed.
+In this section all available classification metrics and related documentation is described. Common for all classifcation functions is that they use the class `factor`.
+
+## A primer on factors
+
+ Consider a classification problem with three classes: `A`, `B`, and `C`. The actual vector of `factor` values is defined as follows:
+
+```{r}
+## set seed
+set.seed(1903)
+
+## actual
+factor(
+    x = sample(x = 1:3, size = 10, replace = TRUE),
+    levels = c(1, 2, 3),
+    labels = c("A", "B", "C")
+)
+```
+
+ Here, the values 1, 2, and 3 are mapped to `A`, `B`, and `C`, respectively. Now, suppose your model does not predict any `B`'s. The predicted vector of `factor` values would be defined as follows:
+
+```{r}
+## set seed
+set.seed(1903)
+
+## predicted
+factor(
+    x = sample(x = c(1, 3), size = 10, replace = TRUE),
+    levels = c(1, 2, 3),
+    labels = c("A", "B", "C")
+)
+```
+
+ In both cases, $k = 3$, determined indirectly by the `levels` argument.

docs/regression_functions.qmd

@@ -1,3 +1,4 @@
 # Regression functions {.unnumbered}
 
-In this section all regression evaluation metrics are listed.
+In this section all available regression metrics and related documentation is described. Common for all regression functions is that they use the class `numeric`.
+