Because the raw value is a very unstandardized way of evaluating the effect of the single features (in the linear regression) and e.g. Cohen's D is easier to interpret.
Edit: I just discovered the package effectsize which do some interesting things.
Currently I am a bit disappointed by the importance measures of RF etc. as you nearly always have collinearity in the independent variables.
I think it could be interesting to introduce the concept of "effect size" in chapter 5.9: https://novustat.com/statistik-blog/effektstaerke-berechnen-beta-koeffizient.html
Because the raw value is a very unstandardized way of evaluating the effect of the single features (in the linear regression) and e.g. Cohen's D is easier to interpret.
Edit: I just discovered the package effectsize which do some interesting things.
Currently I am a bit disappointed by the importance measures of RF etc. as you nearly always have collinearity in the independent variables.