I have recently started studying the basics about regression, and as a beginner I started by Linear Regression.
I read this article that says that for this particular type of regression the relationship between independent and dependent variables has to be linear, which to me implies that I can only predict "lines" with Linear regression: https://www.analyticsvidhya.com/blog/2015/08/comprehensive-guide-regression/
But then I started wondering about how to model functions like "y = log(x)" or "y= sqrt(x)" or "y=exp(x)" or "y=tan(x)" or other nonlinear functions by definition which are not "lines" but "curves".
Then I carried on doing research until I found this article that says that it is not the relationship between the independent and dependent variables that should be linear, but the final functional form passed to the model: https://medium.freecodecamp.org/learn-how-to-improve-your-linear-models-8294bfa8a731
I want to know if that is really the case, and is it always possible to do this "change" in the functional form? Also if it is possible to use linear regression for nonlinear functions, is it still correct to measure the performance of the model using R_square metric?