predict.train vs 使用配方对象进行预测

Question

在指定要在 caret::train 中使用的配方后，我正在尝试预测新样本。我有几个关于这个的问题，因为我在 caret/recipes 文档中找不到。

我应该使用 predict() 还是 predict.train()？有什么区别？
在使用predict之前是否应该先用准备好的recipe烘焙测试数据？当直接在 train() 中使用 preProcess 时，建议您不要预处理新数据，因为 train 对象会自动执行此操作。使用食谱时也是这样吗？

下面是一个可重现的示例，说明了我的过程以及使用 predict 与 predict.train

时的预测差异

library(recipes)
library(caret)
# Data ----
data("credit_data")

credit_train <- credit_data[1:3500,]
credit_test <- credit_data[-(1:3500),]

# Set up recipe ----

set.seed(0)
Rec.Obj = recipe(Status ~ ., data = credit_train) %>%
    step_knnimpute(all_predictors()) %>% 
    step_center(all_numeric())%>%
    step_scale(all_numeric())

# Control parameters ----
set.seed(0)
TC = trainControl("cv",number = 10, savePredictions = "final", classProbs = TRUE, returnResamp = "final")


set.seed(0)
Model.Output = train(Rec.Obj,
                     credit_train,
                     trControl = TC,
                     tuneLength = 1,
                     metric = "Accuracy",
                     method = "glm")

# Preped recipe ----
set.seed(0)
prep.rec <- 
    prep(Rec.Obj, newdata = credit_train)

# Baked data for observation ----
set.seed(0)
bake.train <- bake(prep.rec, new_data = credit_train)
bake.test <- bake(prep.rec, new_data = credit_test)

# investigation of prediction methods ----

# no application of recipe to newdata
set.seed(0)
predict.norm = predict(Model.Output, credit_test, type = "raw")
predict.train = predict.train(Model.Output, credit_test,  type = "raw")

identical(predict.norm,predict.train)
# evaluates to FALSE

# Apply recipe to new data (bake.test)
predict.norm.baked = predict(Model.Output, bake.test, type = "raw")
predict.train.baked = predict.train(Model.Output, bake.test, type = "raw")

identical(predict.norm.baked, predict.train.baked)
# evaluates to FALSE

# Comparison of both predict() funcs
identical(predict.norm, predict.norm.baked)
# evaluates to FALSE

Answer 1

配方嵌入到 train 对象中。答案不同有两个原因：

因为你给了配方（在Model.Output内）处理过的数据要重新处理。您不应该提供 predict() 烘焙数据；只需使用 predict() 并为其提供原始测试集..
让 S3 做它的事情：predict.train 用于 x/y 接口，predict.train.recipe 用于配方接口。只需使用 predict() 即可完成相应的操作。

predict.train vs 使用配方对象进行预测

predict.train vs predict using recipe objects

r

r-caret

predict

r-recipes