[R-SIG-Finance] Random Forest Classifiers

[Zachary Mayer]

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On Nov 26, 2011, at 8:52 PM, Momop Momop <momop540 at yahoo.com> wrote:

>
> Apologies as the� mail got sent before completion. Here's the full text
> �
> I am learning Random Forest and have a basic training question. For my problem, I "derived" various classifiers (var0,var1...var9). They are independent, but the intrinsic values from which they are derived overlap. I get the following data for my RF tree. The question I have is, should I eliminate the number of classifiers that haven't shown enough importance (For example, I could scale %IncMSE relatively and may be just pick the top 3 or 4).
>
> -------------------------------
> %IncMSE    IncNodePurity
> Var0    10.84632    7.232559
> var1    24.53021    7.976509
> var2    26.5005    4.653162
> var3    60.18863    21.882258
> var4    11.97568    7.25413
> var5    49.63468    16.968472
> var6    19.55981    10.009517
> var7    10.36669    13.136694
> var8    14.16585    7.818673
> var9    9.75812    7.178831
> -------------------------------
>
> Essentially, what I was attempting to do was to choose the best derived classifier by eliminating some from the above list which doesn't show noticeable relative impact on MSE. Any guidance or pointers is much appreciated. Thanks!
>
>
> ________________________________
>
> To: "r-sig-finance at r-project.org" <r-sig-finance at r-project.org>
> Sent: Saturday, November 26, 2011 5:45 PM
> Subject: [R-SIG-Finance] Random Forest Classifiers
>
> I am learning Random Forest and have a basic training question. For my problem, I "derived" various classifiers (var0,var1...var9). They are independent, but the intrinsic values from which they are derived overlap. I get the following data for my RF tree. The question I have is, should I eliminate the number of classifiers that haven't shown enough importance (For example, I could scale %IncMSE relatively and may be just pick the top 3 or 4).
>
> -------------------------------
> %IncMSE��� IncNodePurity
> Var0��� 10.84632��� 7.232559
> var1��� 24.53021��� 7.976509
> var2��� 26.5005��� 4.653162
> var3��� 60.18863��� 21.882258
> var4��� 11.97568��� 7.25413
> var5��� 49.63468��� 16.968472
> var6��� 19.55981��� 10.009517
> var7��� 10.36669��� 13.136694
> var8��� 14.16585��� 7.818673
> var9��� 9.75812��� 7.178831
> -------------------------------
>
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