# "Wrong model type for classification" in regression problems in R-Caret

I am trying to use various prediction algorithms from the Caret package in R for a regression problem which is my continuous target variable. Caret thinks that classification is the appropriate class for the problem, and when I submit any of the regression models, the error message "Wrong model type for classification" appears. For reproducibility see Section Data Set for Combined Cycle Power Plant . The data is in CCPP.zip. Let the predictable power depend on other variables. Power is a continuous variable.

```
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)
```

Below is a screenshot:

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For some reason, `caret`

tibles is obfuscated, which is a tidy-versatile variant of the data frame returned `read_excel`

. By converting it to a simple dataframe before passing it to the carriage everything works:

```
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
```

getting:

```
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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