[R] Gini's Importance Value Variable = Inf

[Liaw, Andy]

That result looks fishy:  Not only there shouldn't be Inf, but there
shouldn't be negative values in that measure (look at V6).  I will look into
it.

I hope by now you realize that there's not much point in asking such
package-specific questions on R-help...  Not all package maintainers are on
R-help, and they are the best persons to ask package specific questions or
report bugs.

Andy

> From: Melanie Vida
> 
> Hi All,
> 
> In the script below, the importance measure for column 4 (ie 
> MeanDecreaseGini) indicated "Inf" for V7.
> Running the getTree command showed that "V7" had been 
> selected at least 
> twice in one of the trees for Random Forest. So the "Inf" command was 
> not generated as a result of dividing the sum of the decreases by 0.
> 
> Any suggestions on what may be causing the Inf in "V7" would 
> be helpful?
> Thanks in advance,
> 
> -Melanie
> 
> ---------i
> 
>  library(randomForest)
> 
> credit<-read.csv(url("ftp://ftp.ics.uci.edu/pub/machine-learni
> ng-databases/credit-screening/crx.data"), 
> header=FALSE, na.string="?")
> 
> credit.rf <- randomForest(V16~., credit, imp=T, 
> do.trace=100,na.action=na.omit)
> 
> imp <- round(importance(credit.rf), 2)
> 
> imp
>  -     + MeanDecreaseAccuracy MeanDecreaseGini
> V1   0.00  0.00                 0.00             0.00
> V2   0.75  0.25                 0.55            19.92
> V3   0.41  0.57                 0.46            22.13
> V4   0.39  0.33                 0.33             4.93
> V5   0.26  0.24                 0.21             0.60
> V6   0.39  0.50                 0.40           -46.21
> V7   0.91  0.59                 0.71              Inf
> V8   1.35  1.35                 1.06            37.15
> V9   0.00  0.00                 0.00             0.00
> V10  0.00  0.00                 0.00             0.00
> V11  1.65  1.59                 1.23            49.16
> V12  0.00  0.00                 0.00             0.00
> V13 -0.11 -0.10                -0.10             0.21
> V14  0.82  0.57                 0.66            20.71
> V15  1.36  1.02                 1.01            33.47
> 
> getTree(credit.rf, 1)
> 
>  left daughter right daughter split var split point status prediction
>   [1,]             2              3        15    492.0000     
>  1          0
>   [2,]             4              5        11      2.5000     
>  1          0
>   [3,]             6              7         2     38.5000     
>  1          0
>   [4,]             8              9        14     83.0000     
>  1          0
>   [5,]            10             11         7    207.0000     
>  1          0
>   [6,]            12             13        11      0.5000     
>  1          0
>   [7,]             0              0         0      0.0000     
> -1          2
>   [8,]            14             15         7    117.0000     
>  1          0
>   [9,]            16             17         8      3.0625     
>  1          0
>  [10,]            18             19         3      0.2700     
>  1          0
>  [11,]             0              0         0      0.0000     
> -1          2
>  [12,]            20             21        15   4753.0000     
>  1          0
>  [13,]            22             23         2     37.0850     
>  1          0
>  [14,]            24             25        14      8.5000     
>  1          0
> 
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