Hi
I'm trying to find a correlation between some X predictors and a continuous response variable strongly skewed on the right.
The database contains 140 instances only.
First of all I transformed the response variable by using the function lnx+1.
Then I normalized all the x in the range 0 and 1, reduced the dimensionality with the screening option and then removed the correlated predictors X. At the end of all this process I had 4 predictors only. By running a Random forest without any weigth and the R2 on training data was very low (see first plot below). Then I used a like parabolic weight function (see second plot) e re-ran the Random forest. The result was better as showed in the last plot. Looks like there was a rotation of data around the center of plot. Some one can explain why?
How the weight function should be choosen?
![Picture1.png Picture1.png](https://community.jmp.com/t5/image/serverpage/image-id/26072iD690A6E652279DBF/image-size/large?v=v2&px=999)
![Picture2.png Picture2.png](https://community.jmp.com/t5/image/serverpage/image-id/26073i9D10456F1EDF9B4A/image-size/large?v=v2&px=999)
![Picture3.png Picture3.png](https://community.jmp.com/t5/image/serverpage/image-id/26074i1E33309389367654/image-size/large?v=v2&px=999)