我在运行OS X 10.10.2（Yosemite）的计算机中使用R-studio（版本0.98.994）来应用来自Caret软件包的“随机森林”。这是我的代码：
library(caret)
data(iris)
inTrain <- createDataPartition(y=iris$Species, p=0.7, list=FALSE)
training <- iris[inTrain,]
testing <- iris[-inTrain,]
# Use o Random Forest do CARET
modFit <- train(Species ~ ., data=training, method="rf", prox=TRUE)
modFit
这是错误：
Error in checkInstall(models$library) :
Calls: <Anonymous> ... train.formula -> train -> train.default -> checkInstall
您缺少randomForest图书馆。它是caret该rf方法及其来源的建议库之一。安装后，它应该可以像这样正常工作：
library(randomForest)
library(caret)
data(iris)
inTrain <- createDataPartition(y=iris$Species, p=0.7, list=FALSE)
training <- iris[inTrain,]
testing <- iris[-inTrain,]
# Use o Random Forest do CARET
modFit <- train(Species ~ ., data=training, method="rf", prox=TRUE)
modFit
输出：
> modFit
Random Forest
105 samples
4 predictor
3 classes: 'setosa', 'versicolor', 'virginica'
No pre-processing
Resampling: Bootstrapped (25 reps)
Summary of sample sizes: 105, 105, 105, 105, 105, 105, ...
Resampling results across tuning parameters:
mtry Accuracy Kappa Accuracy SD Kappa SD
2 0.949 0.923 0.0290 0.0436
3 0.953 0.929 0.0305 0.0460
4 0.948 0.921 0.0297 0.0447
Accuracy was used to select the optimal model using the largest value.
The final value used for the model was mtry = 3.
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