R Random Forest prediction not working

Question

I'm new to Random Forests in R, and I'm trying to make a prediction. I have built a Random Forest model using the following code, which works fine

library(randomForest)
RF_model = randomForest(trainrows[,col_truth]~.
                    ,data = trainrows[,cols_to_use]
                    ,ntree=100
                    ,do.trace=T)

If I print out RF_model, I get the following output

Call:
 randomForest(formula = trainrows[, col_truth] ~ ., data = trainrows[,      cols_to_use], ntree = 100, do.trace = T) 
               Type of random forest: classification
                     Number of trees: 100
No. of variables tried at each split: 4

        OOB estimate of  error rate: 19.23%
Confusion matrix:
     0    1 class.error
0 7116 1640   0.1873001
1 1725 7015   0.1973684

Then, when I try and make a prediction with the model, I get the following error

> predict(RF_model)
Error in 1:dim(data)[1] : argument of length 0

I have tried supplying data to the predict method, but I get the same error. Does anyone know what's going on and how to fix it?

EDIT

In order to provide some more data, I have tried using Random Forests with the iris dataset.

rf = randomForest(iris[,1]~., data=iris[,c(1, 2)], ntree=100)
predict(rf)
Error in 1:dim(data)[1] : argument of length 0

This is not related to my data, but a problem with my version of R, I think. Any ideas?

Answer 1

When you use the predict function, you are trying to predict the outcome or labels for your test set.

rf_predict <- predict(RF_model, test_set)

You can create a confusion matrix to compare the accuracy of your random forest by using the table function

table(observed, rf_predict)

Note: The observed will be the correct labels for the test set