Random Forest Example
Error(s), warning(s): randomForest 4.6-12
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Warning message:
In randomForest.default(x, y, mtry = mtryCur, ntree = ntreeTry, :
invalid mtry: reset to within valid range
'data.frame': 150 obs. of 5 variables:
$ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
$ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
$ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
$ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
$ Species : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 5.1 3.5 1.4 0.2 setosa
2 4.9 3.0 1.4 0.2 setosa
3 4.7 3.2 1.3 0.2 setosa
4 4.6 3.1 1.5 0.2 setosa
5 5.0 3.6 1.4 0.2 setosa
6 5.4 3.9 1.7 0.4 setosa
setosa versicolor virginica
setosa 39 0 0
versicolor 0 34 2
virginica 0 2 29
Call:
randomForest(formula = Species ~ ., data = TrainData, ntree = 100, proximity = TRUE)
Type of random forest: classification
Number of trees: 100
No. of variables tried at each split: 2
OOB estimate of error rate: 3.77%
Confusion matrix:
setosa versicolor virginica class.error
setosa 39 0 0 0.00000000
versicolor 0 34 2 0.05555556
virginica 0 2 29 0.06451613
Test setosa versicolor virginica
setosa 11 0 0
versicolor 0 14 4
virginica 0 0 15
mtry = 2 OOB error = 4.67%
Searching left ...
mtry = 4 OOB error = 4%
0.1428571 0.05
mtry = 8 OOB error = 4%
0 0.05
Searching right ...
mtry = 1 OOB error = 5.33%
-0.3333333 0.05
mtry OOBError
1.OOB 1 0.05333333
2.OOB 2 0.04666667
4.OOB 4 0.04000000
8.OOB 8 0.04000000
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