Читать книгу Machine Learning For Dummies - John Paul Mueller, John Mueller Paul, Luca Massaron - Страница 38

As you progress through the book, you discover the need to teach whichever algorithm you’re using (don’t worry about specific algorithms; you see a number of them later in the book) how to recognize various kinds of data and then to do something interesting with it. This training process ensures that the algorithm reacts correctly to the data it receives after the training is over. Of course, you also need to test the algorithm to determine whether the training is a success. In many cases, the book helps you discover ways to break a data source into training and testing data components in order to achieve the desired result. Then, after training and testing, the algorithm can work with new data in real time to perform the tasks that you verified it can perform.

In some cases, you might not have enough data at the outset for both training (the essential initial test) and testing. When this happens, you might need to create a test setup to generate more data, rely on data generated in real time, or create the test data source artificially. You can also use similar data from existing sources, such as a public or private database. The point is that you need both training and testing data that will produce a known result before you unleash your algorithm into the real world of working with uncertain data.