Chapter 3. Multiple Regression in Action
In the previous chapter, we introduced linear regression as a supervised method for machine learning rooted in statistics. Such a method forecasts numeric values using a combination of predictors, which can be continuous numeric values or binary variables, given the assumption that the data we have at hand displays a certain relation (a linear one, measurable by a correlation) with the target variable. To smoothly introduce many concepts and easily explain how the method works, we limited our example models to just a single predictor variable, leaving to it all the burden of modeling the response.
However, in real-world applications, there may be some very important causes determining the events you want to model but it is indeed rare that a single variable could take the stage alone and make a working predictive model. The world is complex (and indeed interrelated in a mix of causes and effects) and often it cannot be easily explained without...
