week43 update

2020-10-23 05:56:41 +02:00
parent 83da23092b
commit 74a57bd64a
7 changed files with 6 additions and 6 deletions
@@ -3204,7 +3204,7 @@ After fitting the PCA transformer to the dataset, you can access the principal c
 components variable (note that it contains the PCs as horizontal vectors, so, for example, the first
 principal component is equal to 
 !bc pycod
-pca.components_.T[:, 0].
+pca.components_.T[:, 0]
 !ec
 Another very useful piece of information is the explained variance ratio of each principal component,
 available via the $explained\_variance\_ratio$ variable. It indicates the proportion of the dataset’s