Discussing a multiple regression model

On this part we shall consider a curious example, I must say. As said our reference for this case study [7, p. 74]: "If we were the only ones in the world with access to this info, we could be the best Boston real-estate investors in 1978! Unless, somehow, someone were able to build an even more accurate estimate . . This is the Boston House problem. Essentially, the problem is used as benchmark for machine learning, generally, on competitions. "to estimate the median value of the house prices in a neighborhood (MEDV) given all the input features from the neighborhood." This problem is different from the previous one only because we have several inputs instead of just one. This problem is closer from reality since most problem, at least the one that can be useful, will have to do more than humans can do either with simple models or by head; and machine learning is good at it! As long as you have the computer power, and time to wait, they solve it with their feet on their backs, if they have any! One interesting reflection we shall do is regarding interpreting their inner workings, beyond just prediction. Prediction is the process by which we want to know what is next in time, on a system (e.g., stock market or demands on a company). "Is there any way to peek inside the model to see how it understands the data?….