randomForest output is based on predict(iris.rf) whereas the
code shown below uses predict(iris.rf, iris). See ?predict.randomForest
for an explanation.
On Thu, Feb 26, 2009 at 11:10 AM, Li GUO <guoli84 at yahoo.com>
wrote:> Dear R users,
>
> I have a question on the confusion matrix generated by function
randomForest.
> I used the entire data
> set to generate the forest, for example:
>> print(iris.rf)
>
> Call:
> ?randomForest(formula = Species ~ ., data = iris, importance = TRUE,
> keep.forest = TRUE)
>
> confusion
> ? ? ? ? ? setosa versicolor virginica class.error
> setosa ? ? ? ? 50 ? ? ? ? ?0 ? ? ? ? 0 ? ? ? ?0.00
> versicolor ? ? ?0 ? ? ? ? 47 ? ? ? ? 3 ? ? ? ?0.06
> virginica ? ? ? 0 ? ? ? ? ?3 ? ? ? ?47 ? ? ? ?0.06
>
> then I classified the same data set with this forest:
>
>> iris.pred <- predict(iris.rf, iris)
>> table(observed = iris[,"Species"], predicted = iris.pred)
> ? ? ? ? ? ?predicted
> observed ? ? setosa versicolor virginica
> ?setosa ? ? ? ? 50 ? ? ? ? ?0 ? ? ? ? 0
> ?versicolor ? ? ?0 ? ? ? ? 50 ? ? ? ? 0
> ?virginica ? ? ? 0 ? ? ? ? ?0 ? ? ? ?50
> Why the two matrices are different?
> Thinks,
>
> Li
>
>
>
>
> ? ? ? ?[[alternative HTML version deleted]]
>
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