Understanding feature engineering
Feature engineering, as the name suggests, is the process of engineering features from data, mostly using domain knowledge, to make the learning process smoother and more efficient. In a typical ML setting, engineering good features is essential to get good performance from any ML model. Feature engineering is a highly subjective part of ML, where each problem at hand has a different path of solution—one that is handcrafted for that problem. Suppose you have a dataset of house prices and you have a feature, Year Built, which tells you the year the house was built. Now, to make the information better, we can create another feature, House Age, from the Year Built feature. This may give the model better information, and this is called feature engineering.
When we are casting a time series problem as a regression problem, there are a few standard techniques that we can apply. This is a key step in the process, as how well an ML model acquires...
