When doing statistics with linear regression, p-values and statistical significance are fundamental aspects for building a linear regression model.
But unlike in statistics, machine learning is all about making correct predictions. The more variables we add to a linear regression model, the higher we expect the machine learning accuracy to be.
Would you agree that p values and an analysis of statistical significance are not important during machine learning model evaluation when building a linear regression?
I ask because my professor during my masters degree made that point but I have seen data scientists spend a lot of time discussing p values.