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edited Oct 28, 2014 at 15:52

As it is generally believed that non-linear models should not be used unless absolutely necessary. I don't think this is accurate.
GLMs are fairly flexible. Are you including regression splines, interactions... in your model?
Regularization is often used with GLMs - the number of parameters don't need to be fixed.
As mentioned by Scortchi, I don't think you can prove that any linear model will be outperformed by random forest - given the large number of options available.

As it is generally believed that non-linear models should not be used unless absolutely necessary. I don't think this is accurate.
GLMs are fairly flexible. Are you including regression splines, interactions... in your model?
Regularization is often used with GLMs - the number of parameters don't need to be fixed.
As mentioned by Scortchi, I don't think you can prove that any linear model will be outperformed by random forest - given the large number of options available.

As it is generally believed that non-linear models should not be used unless absolutely necessary. I don't think this is accurate.
GLMs are fairly flexible. Are you including regression splines, interactions... in your model?
Regularization is often used with GLMs - the number of parameters don't need to be fixed.
As mentioned by Scortchi, I don't think you can prove that any linear model will be outperformed by random forest - given the large number of options available.

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created Oct 28, 2014 at 15:46

As it is generally believed that non-linear models should not be used unless absolutely necessary. I don't think this is accurate.
GLMs are fairly flexible. Are you including regression splines, interactions... in your model?
Regularization is often used with GLMs - the number of parameters don't need to be fixed.
As mentioned by Scortchi, I don't think you can prove that any linear model will be outperformed by random forest - given the large number of options available.