When we talked about random forests, that was an example of ensemble learning, where we're actually combining multiple models together to come up with a better result than any single model could come up with. So, let's learn about that in a little bit more depth. Let's talk about ensemble learning a little bit more.
So, remember random forests? We had a bunch of decision trees that were using different subsamples of the input data, and different sets of attributes that it would branch on, and they all voted on the final result when you were trying to classify something at the end. That's an example of ensemble learning. Another example: when we were talking about k-means clustering, we had the idea of maybe using different k-means models with different initial random centroids, and letting them all vote on the final result as well. That is...
