The good thing about data is that there's a lot of it in the world. The bad thing is that it's hard to process this data. The challenges stem from the diversity and noisiness of the data. We humans usually process data coming into our ears and eyes. These inputs are transformed into electrical or chemical signals. On a very basic level, computers and robots also work with electrical signals. These electrical signals are then translated into ones and zeroes. However, we program in Python in this book and, on that level, normally we represent the data either as numbers, images, or texts. Actually, images and text aren't very convenient, so we need to transform images and text into numerical values.
Especially in the context of supervised learning, we have a scenario similar to studying for an exam. We have a set of practice questions and the actual exams. We should be able to answer exam questions without knowing the answers to them. This is called generalization—we learn something from our practice questions and, hopefully, are able to apply the knowledge to other similar questions. In machine learning, these practice questions are called training sets or training samples. They're where the models derive patterns from. And the actual exams are testing sets or testing samples. They're where the models eventually apply and how compatible they are is what it's all about. Sometimes, between practice questions and actual exams, we have mock exams to assess how well we'll do in actual ones and to aid revision. These mock exams are called validation sets or validation samples in machine learning. They help us to verify how well the models will perform in a simulated setting, then we fine-tune the models accordingly in order to achieve greater hits.
An old-fashioned programmer would talk to a business analyst or other expert, then implement a rule that adds a certain value multiplied by another value corresponding, for instance, to tax rules. In a machine learning setting, we give the computer example input values and example output values. Or if we're more ambitious, we can feed the program the actual tax texts and let the machine process the data further, just like an autonomous car doesn't need a lot of human input.
This means implicitly that there's some function, for instance, a tax formula, we're trying to figure out. In physics, we have almost the same situation. We want to know how the universe works and formulate laws in a mathematical language. Since we don't know the actual function, all we can do is measure the error produced and try to minimize it. In supervised learning tasks, we compare our results against the expected values. In unsupervised learning, we measure our success with related metrics. For instance, we want clusters of data to be well defined; the metrics could be how similar the data points within one cluster are, and how different the data points from two clusters are. In reinforcement learning, a program evaluates its moves, for example, using some predefined function in a chess game.
Other than the normal generalizing with data, there can be two levels of generalization, over and under generalization, which we'll explore in the next section.