Sometimes, you'll find yourself in a situation where features and target variables are not really related. In this case, you can modify the input dataset. You can apply linear or nonlinear transformations that can improve the accuracy of the system, and so on. It's a very important step for the overall process because it completely depends on the skills of the data scientist, who is the one responsible for artificially changing the dataset and shaping the input data for a better fit for the learning model. Although this step intuitively just adds complexity, this approach often boosts the performance of the learner; that's why it is used by bleeding-edge techniques, such as deep learning.
For example, if you're trying to predict the value of a house and you know the height, width, and the length of each room, you can artificially build...
