Until now, we have spent all of our time talking about categorical outcomes and most of those examples apply to continuous outcomes, but in this section we're going to focus exclusively on continuous outcome variables.
As I mentioned previously, when we're talking about continuous outcome predictions or variables, everything that we've talked about in this book still applies: the main difference, though, is going to be in terms of how we end up combining predictions.
Here, in this example, we can see that we built three models and we have predictions from each one of those models:
When we want to combine the predictions, all we do is take a mathematical average. The mean of these previous models ends up being the combined prediction because we're not predicting individual categories as we were when we had a categorical outcome...
