I have an R dataframe with 9 input variables and 1 output variable. I want to find the accuracy of randomForest using each individual input, and add them to a list. To do this, I need to loop over a list of formulas, as in the code below:
library(randomForest)
library(caret)
formulas = c(target ~ age, target ~ sex, target ~ cp,
target ~ trestbps, target ~ chol, target ~ fbs,
target ~ restecg, target ~ ca, target ~ thal)
test_idx = sample(dim(df)[1], 60)
test_data = df[test_idx, ]
train_data = df[-test_idx, ]
accuracies = rep(NA, 9)
for (i in 1:length(formulas)){
rf_model = randomForest(formulas[i], data=train_data)
prediction = predict(rf_model, newdata=test_data, type="response")
acc = confusionMatrix(test_data$target, prediction)$overall[1]
accuracies[i] = acc
}
I run into an error,
Error in if (n==0) stop("data (x) has 0 rows") : argument is of length zero calls: ... eval -> eval -> randomForest -> randomForest.default Execution halted
The error is related to the formulas[i] argument passed to randomForest, when I type the formula name as the argument (for example, rf_model = randomForest(target ~ age, data=train_data), there is no error.
Is there any other way to iterate over randomForest?
Thank you!
As you have not provided any data, I am using the iris dataset. You have to make 2 changes in your code to make it run. First, use list to store the formulas, and second, formulas[[i]] within for loop. You can use the following code
library(randomForest)
library(caret)
df <- iris
formulas = list(Species ~ Sepal.Length, Species ~ Petal.Length, Species ~ Petal.Width,
Species ~ Sepal.Width)
test_idx = sample(dim(df)[1], 60)
test_data = df[test_idx, ]
train_data = df[-test_idx, ]
accuracies = rep(NA, 4)
for (i in 1:length(formulas)){
rf_model = randomForest(formulas[[i]], data=train_data)
prediction = predict(rf_model, newdata=test_data, type="response")
acc = confusionMatrix(test_data$Species, prediction)$overall[1]
accuracies[i] = acc
}
#> 0.7000000 0.9166667 0.9166667 0.5000000