使用随机林创建二进制结果

首先,正如Tim Biegeleisen所说,你应该将你的Gewonnen变量转换为一个因子(在训练和测试集中),如果它还没有:

training$Gewonnen <- as.factor(training$Gewonnen)
testing$Gewonnen <- as.factor(testing$Gewonnen)

之后,函数中的type选项确定您为二进制分类问题获得的响应类型,即类标签或概率.以下是使用包中数据集的文档中可重现的示例:caretpredictcaretSonarmlbench

library(caret)
library(mlbench)
data(Sonar)
str(Sonar$Class)
# Factor w/ 2 levels "M","R": 2 2 2 2 2 2 2 2 2 2 ...

set.seed(998)
inTraining <- createDataPartition(Sonar$Class, p = .75, list = FALSE)
training <- Sonar[ inTraining,]
testing  <- Sonar[-inTraining,]

modFit <- train(Class ~ ., data=training, method="rf", prox=TRUE)

pred <- predict(modFit, testing, type="prob") # for class probabilities
head(pred)
#        M     R
# 5  0.442 0.558
# 10 0.276 0.724
# 11 0.096 0.904
# 12 0.360 0.640
# 20 0.654 0.346
# 21 0.522 0.478

pred2 <- predict(modFit, testing, type="raw") # for class labels
head(pred2)
# [1] R R R R M M
# Levels: M R

对于混淆矩阵,您将需要类标签(即pred2上面):

confusionMatrix(pred2, testing$Class)
# Confusion Matrix and Statistics

#           Reference
# Prediction  M  R
#          M 25  6
#          R  2 18