Dealing with too much data
It's true that more data is usually better, but this isn't always the case. There are many times when having extra data has a negative impact on an outcome. Such a case was covered in Chapter 1, Understanding the AI/ML Landscape, where a father gave his child an extra example of what a tiger was, but that extra example was actually of a panther. That additional bit of information would then turn into a negative addition to the training set and create a worse learning outcome for your model.
How are you supposed to know this? Understand the data. This will be a common theme in this chapter, the book, and in the real world. If you don't start there, then everything else is more challenging. It's similar to being able to understand bias, as discussed in Chapter 6, Overcoming Bias in AI/ML.
Sometimes though, you won't or can't have a full grasp of the data, but you can use tools to help you out. The first clue that you can...
