I am applying alternating conditional expectations (ACE) in a forward stepwise manner, similarly to the authors of the original ACE paper.

My dataset has 103 predictor variables $x_i$, one response variable $y$, 255 samples. The distribution of response values is as follows:

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I am trying to select those predictor variables that maximize $R^2$. However, ACE finds the transformations of the response variable something like this:

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Even though $R^2=0.83$ here, the transformation of $y$ lumps most of the data near zero. So, if I zoom the figure on the right near zero, I get very small correlation.

How to avoid it? How to make ACE transform the response variable closer to linear?


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