I am dealing with a dataset in which there are 493 observations spanned over 30 predictors. My intention is to fit a model to make accurate predictions.

It seems to me that the ratio $\frac{n}{p}$ is relatively small to fit a regression model (correct me if I'm wrong about this); therefore, I am trying to fit a tree model (bagging, random forest, or boosting) to the dataset.

My question is does tree-based models also suffer from the stability-issues that result from a low $\frac{\text{Number of observations}}{\text{Number of predictors}}$ ratio as regression models do? Is this ratio an important factor in tree-based methods (assume it matters at all)? Why or why not?

Any suggestions about relevant reading/literatures would also be appreciated.

  • $\begingroup$ This could be a duplicate; you could benefit from searching earlier threads. Also, here is a very recent related thread (specific to VAR models but can be generalized). $\endgroup$ Jul 25 '16 at 8:43
  • $\begingroup$ Thanks for the suggestion. I guess I'm more interested in the role $\frac{\text{Number of observations}}{\text{Number of predictors}}$ plays in tree-based method, which I did not find any reference from earlier threads. Already edited the question. $\endgroup$
    – Jack Shi
    Jul 25 '16 at 8:54

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