I am working on an assignment where I have to evaluate the predictive model based on RMSE (Root Mean Squared Error) using the test data. I have already built a linear regression model to predict wine quality (numeric) using all available predictor variables based on the train data. Below is my current code. The full error is "Error: Problem with mutate() column regression1. i regression1 = predict(regression1, newdata = my_type_test). x no applicable method for 'predict' applied to an object of class "c('double', 'numeric')"

install.packages("rsample")
library(rsample)

my_type_split <- initial_split(my_type, prop = 0.7)
my_type_train <- training(my_type_split)
my_type_test <- testing(my_type_split)  

my_type_train

regression1 <- lm(formula = quality ~ fixed.acidity   volatile.acidity   citric.acid   chlorides   free.sulfur.dioxide   total.sulfur.dioxide  
                  density   pH   sulphates   alcohol, data = my_type_train)

summary(regression1)
regression1

install.packages("caret")
library(caret)
install.packages("yardstick")
library(yardstick)
library(tidyverse)

my_type_test <- my_type_test %>% 
  mutate(regression1 = predict(regression1, newdata = my_type_test)) %>%
  
rmse(my_type_test, price, regression1)

CodePudding user response：

Many of the steps you take are probably unnecessary.
A minimal example that should achieve the same thing:

# Set seed for reproducibility
set.seed(42)
# Take the internal 'mtcars' dataset
data <- mtcars
# Get a random 80/20 split for the number of rows in data
split <- sample(
   ​size = nrow(data), 
   ​x = c(TRUE, FALSE), 
   ​replace = TRUE,
   ​prob = c(0.2, 0.8)
)
# Split the data into train and test sets
train <- data[split, ]
test <- data[!split, ]

# Train a linear model
fit <- lm(mpg ~ disp   hp   wt   qsec   am   gear, data = train)

# Predict mpg in test set
prediction <- predict(fit, test)

Result:

> caret::RMSE(prediction, test$mpg)
[1] 4.116142