我的训练数据集（train）是一个包含n-features和一个额外结果列y的数据框架。我构建了3个独立的模型，例如：
m1 <- train(y ~ ., data = train, method = "lda")m2 <- train(y ~ ., data = train, method = "rf")m3 <- train(y ~ ., data = train, method = "gbm")使用测试数据集（test）我可以评估这些独立模型的质量（自然，它也包含结果y）：
pred1 <- predict(m1, newdata = test)pred2 <- predict(m2, newdata = test)pred3 <- predict(m3, newdata = test)如果我将每个独立模型应用于一个结果未知的DATA_TO_PREDICT数据框架（包含5个示例），每个独立模型的输出自然是5个预测结果：
predict(m1, DATA_TO_PREDICT)predict(m2, DATA_TO_PREDICT)predict(m3, DATA_TO_PREDICT)现在我想使用R-Caret包中的随机森林来构建一个组合模型：
DF <- data.frame(pred1, pred2, pred3, y = test$y)MODEL <- train(y ~ ., data = DF, method = "rf")我观察到组合模型的准确率有所提高：
predMODEL <- predict(MODEL, DF)但是，当我将组合模型应用于DATA_TO_PREDICT（结果未知）时，输出不仅有5个预测结果，而是有一大串重复的结果，数量超过百个。我使用了：
predict(MODEL, newdata = DATA_TO_PREDICT)示例：
这里我展示了一个具体的例子，输出是错误的。也就是说，我想预测4个新数据，但我得到的结果却有数十个输出：
library(caret)library(gbm)set.seed(10)library(AppliedPredictiveModeling)data(AlzheimerDisease)adData = data.frame(diagnosis,predictors)inTrain = createDataPartition(adData$diagnosis, p = 3/4)[[1]]training = adData[ inTrain,]testing = adData[-inTrain,]inTEST <- (5:nrow(testing))test <- testing[inTEST,]DATA_TO_PREDICT <- testing[-inTEST,]m1 <- train(diagnosis ~ ., data=training, method="rf")m2 <- train(diagnosis ~ ., data=training, method="gbm")m3 <- train(diagnosis ~ ., data=training, method="lda")p1 <- predict(m1, newdata = test)p2 <- predict(m2, newdata = test)p3 <- predict(m3, newdata = test)DF <- data.frame(p1, p2, p3, diagnosis = test$diagnosis)MODEL <- train(diagnosis ~ ., data = DF, method = "rf")predMODEL <- predict(MODEL, DF)然后，如果我构建了组合模型：
pred1 <- predict(m1, DATA_TO_PREDICT)pred2 <- predict(m2, DATA_TO_PREDICT)pred3 <- predict(m3, DATA_TO_PREDICT)DF2 <- data.frame(pred1, pred2, pred3)predict(MODEL, newdata = DF2) 请注意，DATA_TO_PREDICT只有4个示例，而输出是：
[1] Control Control Control Control Control Control Control Control [9] Control Control Control Control Control Control Control Control [17] Control Control Control Control Control Control Control Control [25] Control Control Control Control Control Control Control Control [33] Control Control Control Control Control Control Control Control [41] Control Control Control Control Control Control Control Control [49] Control Control Control Control Control Control Control Control [57] Control Control Control Control Control Control Control Control [65] Control Control Control Control Control Control Control Control [73] Control Control Control Control Control Control Levels: Impaired Control回答：
这是因为MODEL是在三个独立模型（pred1、pred2和pred3）对测试数据的预测结果上训练的，而在最后一步中，DATA_TO_PREDICT被提供给MODEL，但它实际上包含的是观测值。首先，需要存储独立模型对DATA_TO_PREDICT的预测值，然后将这些预测值作为newdata用于MODEL。
#（示例开头部分已省略）DF <- data.frame(p1, p2, p3, diagnosis = test$diagnosis)# 这是在使用预测结果作为输入来训练模型：MODEL <- train(diagnosis ~ ., data = DF, method = "rf")# 这部分缺失 ----------------------# 为了获得组合模型的输入# 需要对DATA_TO_PREDICT进行预测p1b <- predict(m1, newdata = DATA_TO_PREDICT)p2b <- predict(m2, newdata = DATA_TO_PREDICT)p3b <- predict(m3, newdata = DATA_TO_PREDICT)DFb <- data.frame(p1b, p2b, p3b)colnames(DFb) <- c("p1", "p2", "p3")#----------------------------------------predMODEL <- predict(MODEL, DFb)# [1] Control Control Control Control 