Add missing word

model-building.Rmd

@@ -8,7 +8,7 @@ We will take advantage of the fact that you can think about a model partitioning
 
 For very large and complex datasets this will be a lot of work. There are certainly alternative approaches - a more machine learning approach is simply to focus on the predictive ability of the model. These approaches tend to produce black boxes: the model does a really good job at generating predictions, but you don't know why. This is a totally reasonable approach, but it does make it hard to apply your real world knowledge to the model. That, in turn, makes it difficult to assess whether or not the model will continue to work in the long-term, as fundamentals change. For most real models, I'd expect you to use some combination of this approach and a more classic automated approach.
 
-It's a challenge to know when to stop. You need to figure out when your model is good enough, and when additional investment is unlikely to pay off. I particularly this quote from reddit user Broseidon241: 
+It's a challenge to know when to stop. You need to figure out when your model is good enough, and when additional investment is unlikely to pay off. I particularly like this quote from reddit user Broseidon241: 
 
 > A long time ago in art class, my teacher told me "An artist needs to know 
 > when a piece is done. You can't tweak something into perfection - wrap it up.