Estimating feature importance
After having confirmed the values of the coefficients of the linear model we have built, and after having explored the basic statistics necessary to understand if our model is working correctly, we can start auditing our work by first understanding how a prediction is made up. We can obtain this by accounting for each variable's role in the constitution of the predicted values. A first check to be done on the coefficients is surely on the directionality they express, which is simply dictated by their sign. Based on our expertise on the subject (so it is advisable to be knowledgeable about the domain we are working on), we can check whether all the coefficients correspond to our expectations in terms of directionality. Some features may decrease the response as we expect, thereby correctly confirming that they have a coefficient with a negative sign, whereas others may increase it, so a positive coefficient should be correct. When coefficients do not correspond...
