removes part of sentence

manuscript/04.2-interpretable-linear.Rmd

@@ -316,4 +316,4 @@ Step-wise procedures:
 - Backward selection: Same as forward selection, but instead of adding features, start with the model that includes all features and try out which feature you have to remove  to get the highest performance increase. Repeat until some stopping criterium is reached.
 
 I recommend using Lasso, because it can be automated, looks at all features at the same time and can be controlled via $\lambda$.
-It also works for the [logistic regression model](#logistic) for classification, which is the topic of Chapter 4.3.
+It also works for the [logistic regression model](#logistic) for classification.