Linear regression is one of the world's oldest machine learning concepts. Invented in the early nineteenth century, it is still one of the more vulnerable methods of understanding the relationship between input and output.
The ideas behind linear regression is familiar to us all. We feel that some things are correlated with one another. Sometimes they are causal in nature. There exists a very fine line between correlation and causation. For example, summer sees more sales in ice creams and cold beverages, while winter sees more sales in hot cocoa and coffee. We could say that the seasons themselves cause the amount of sales—they're causal in nature. But are they really?
Without further analysis, the best thing we can say is that they are correlated with one another. The phenomenon of summer is connected to the phenomenon...
