Combining models
A model takes in data (usually preprocessed) and produces predictive results. What if we employ multiple models; will we make better decisions by combining predictions from individual models? We will talk about this in this section.
Let's start with an analogy. In high school, we sit together with other students and learn together, but we aren't supposed to work together during the exam. The reason is, of course, that teachers want to know what we've learned, and if we just copy exam answers from friends, we may not have learned anything. Later in life, we discover that teamwork is important. For example, this book is the product of a whole team, or possibly a group of teams.
Clearly, a team can produce better results than a single person. However, this goes against Occam's razor, since a single person can come up with simpler theories compared to what a team will produce. In machine learning, we nevertheless prefer...
