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R-Caret回归问题中的“错误的分类模型类型”

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我试图使用R中Caret包中的各种预测算法来回归问题，即我的目标变量是连续的 . Caret认为分类是问题的合适类，当我通过任何回归模型时，我收到一条错误消息，上面写着"wrong model type for classification" . 为了重现性，让我们看看Combined Cycle Power Plant Data Set . 数据在CCPP.zip中 . 让我们将功率预测为其他变量的函数 . 权力是一个连续变量 .

library(readxl)
  library(caret)
  power_plant = read_excel("Folds5x2_pp.xlsx")
  apply(power_plant,2, class)   # shows all columns are numeric

  control <- trainControl(method="repeatedcv", number=10, repeats=5)

  my_glm <- train(power_plant[,1:4], power_plant[,5],
           method = "lm",
           preProc = c("center", "scale"),
            trControl = control)

下面的图片是我的截图：

enter image description here

r r-caret

1 回答

  • 1

    出于某种原因， caret 被tibbles弄糊涂了，这是 read_excel 返回的数据帧的tidyverse变体 . 通过在将其转换为插入符号之前将其转换为简单的数据帧，一切正常：

    library(readxl)
library(caret)
power_plant = read_excel("Folds5x2_pp.xlsx")
apply(power_plant,2, class)   # shows all columns are numeric

power_plant <- data.frame(power_plant)
control <- trainControl(method="repeatedcv", number=10, repeats=5)

my_glm <- train(power_plant[,1:4], power_plant[,5],
                method = "lm",
                preProc = c("center", "scale"),
                trControl = control)

my_glm

    收益：

    Linear Regression 

9568 samples
   4 predictor

Pre-processing: centered (4), scaled (4) 
Resampling: Cross-Validated (10 fold, repeated 5 times) 
Summary of sample sizes: 8612, 8612, 8611, 8612, 8612, 8610, ... 
Resampling results:

  RMSE      Rsquared 
  4.556703  0.9287933

Tuning parameter 'intercept' was held constant at a value of TRUE
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