R-Caret：如何使用多个模型构建更高效的模型并预测新结果

2019年8月5日 363次阅读

我的训练数据集(训练)是一个具有n个特征的数据框和一个具有结果y的附加列.我建立了3个人模型,例如：

m1 <- train(y ~ ., data = train, method = "lda")
m2 <- train(y ~ ., data = train, method = "rf")
m3 <- train(y ~ ., data = train, method = "gbm")

通过测试数据集(测试),我可以评估这些个体模型的质量(当然,它有结果y)：

pred1 <- predict(m1, newdata = test)
pred2 <- predict(m2, newdata = test)
pred3 <- predict(m3, newdata = test)

如果我使用5个示例在数据框架DATA_TO_PREDICT(结果未知)中应用每个单独的模型,则输出自然是每个模型的5个预测：

predict(m1, DATA_TO_PREDICT)
predict(m2, DATA_TO_PREDICT)
predict(m3, DATA_TO_PREDICT)

现在我想使用R-Caret-Package和Random Forest的组合模型：

DF <- data.frame(pred1, pred2, pred3, y = test$y)
MODEL <- train(y ~ ., data = DF, method = "rf")

我可以观察到组合模型的准确性增加了：

predMODEL <- predict(MODEL, DF)

但是如果我在DATA_TO_PREDICT中应用组合模型(结果是未知的),则输出不仅有5个预测,而且是具有重复结果且大于100的巨大列表.我用过：

predict(MODEL, newdata = DATA_TO_PREDICT)

例：

这里我展示了输出错误的具体示例.也就是说,我想预测4个新数据,但我得到了几十个输出的结果：

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,输出为：

  [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

最佳答案 这是因为MODEL训练了三个单独模型的预测(测试数据的pred1,pred2和pred3),并且在最后一步中,DATA_TO_PREDICT被提供给MODEL,而MODEL则由观察组成.首先,必须存储DATA_TO_PREDICT的各个模型的预测值,然后将其用作MODEL的新数据.

# (Beginning of the example omitted)
DF <- data.frame(p1, p2, p3, diagnosis = test$diagnosis)
# This trains a model with predictions as inputs:
MODEL <- train(diagnosis ~ ., data = DF, method = "rf")

# This is missing ----------------------
# To get the inputs for the ensemble model
# the predictions for DATA_TO_PREDICT are needed
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
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