R-Caret：如何使用多个模型构建更高效的模型并预测新结果

Question

我的训练数据集 (train) 是一个具有 n-features 的数据框和一个包含结果 y。我建立了 3 个个体模型，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) 我可以评估这些个体模型的质量（当然，它的结果是 y） :

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

如果我在数据框中应用每个单独的模型 DATA_TO_PREDICT（结果未知）有 5 个示例，输出自然是每个单独模型的 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 中的组合模型和随机森林：

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度增加了：

predMODEL <- predict(MODEL, DF)

但如果我在 DATA_TO_PREDICT 中应用组合模型（结果未知），输出不仅有 5 个预测，而且有一个巨大的列表，其中有重复的结果大于百。我用过：

predict(MODEL, newdata = DATA_TO_PREDICT)

示例：

这里我给出一个输出错误的具体例子。也就是说，我想预测 4 new 数据，但我得到的结果有几十个输出：

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)

那么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

Answer 1

这是因为 MODEL 是根据三个单独模型（测试数据的 pred1、pred2 和 pred3 的预测进行训练的，并且在最后步骤 DATA_TO_PREDICT 被提供给 MODEL 而是由观察组成。首先，必须存储 DATA_TO_PREDICT 的各个模型的预测值，然后用作 MODEL 的 newdata。

# (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

R-Caret：如何使用多个模型构建更高效的模型并预测新结果

R-Caret: how to build a more efficient model with multiple models and predict new results

r

machine-learning

r-caret

示例：