UBC-DSCI
diff --git a/‎source/_toc.yml
+1-1 b/‎source/_toc.yml
+1-1
diff --git a/‎source/classification1.md
+1-4 b/‎source/classification1.md
+1-4
@@ -9,7 +9,7 @@ parts:
     - file: acknowledgements-python.md
     - file: authors.md
     - file: editors.md
-    #- file: setup.md
+    - file: setup.md
 - caption: Chapters
   numbered: 3
   chapters:
 
@@ -942,7 +942,6 @@ we will discuss how to choose $K$ in the next chapter.
 > which weigh each neighbor's vote differently, can be found on 
 > [the `scikit-learn` website](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html?highlight=kneighborsclassifier#sklearn.neighbors.KNeighborsClassifier).
 
-
 ```{code-cell} ipython3
 knn = KNeighborsClassifier(n_neighbors=5)
 knn
@@ -1048,7 +1047,6 @@ unscaled_cancer['Class'] = unscaled_cancer['Class'].replace({
    'B' : 'Benign'
 }).astype('category')
 unscaled_cancer
-unscaled_cancer
 ```
 
 Looking at the unscaled and uncentered data above, you can see that the differences
@@ -1146,7 +1144,7 @@ is to *drop* the remaining columns. This default behavior works well with the re
 in the {ref}`08:puttingittogetherworkflow` section), but for visualizing the result of preprocessing it can be useful to keep the other columns
 in our original data frame, such as the `Class` variable here.
 To keep other columns, we need to set the `remainder` argument to `'passthrough'` in the `make_column_transformer` function.
- Furthermore, you can see that the new column names---{glue:}`scaled-cancer-column-0`
+Furthermore, you can see that the new column names---{glue:}`scaled-cancer-column-0`
 and {glue:}`scaled-cancer-column-1`---include the name
 of the preprocessing step separated by underscores. This default behavior is useful in `sklearn` because we sometimes want to apply
 multiple different preprocessing steps to the same columns; but again, for visualization it can be useful to preserve
@@ -1742,7 +1740,6 @@ unscaled_cancer['Class'] = unscaled_cancer['Class'].replace({
 }).astype('category')
 unscaled_cancer
 
-
 # create the KNN model
 knn = KNeighborsClassifier(n_neighbors=7)