Prediction problem in randomForest in R

Question

I'm trying to use the randomForest algorithm in R. When I apply the algorithm to my database, I got the following result:

library(randomForest)

x = data[,1:8]
y = data[,9]

model <- randomForest(y ~ ., x)
model

Call:
 randomForest(formula = y ~ ., data = x) 
               Type of random forest: classification
                     Number of trees: 500
No. of variables tried at each split: 2

        OOB estimate of  error rate: 23.7%
Confusion matrix:
    0   1 class.error
0 430  70   0.1400000
1 112 156   0.4179104

The answer is according to the prediction:

pred <- predict(model, type = "class")
table(pred, y)
1-mean(pred==y)

  y
pred   0   1
   0 430 112
   1  70 156
[1] 0.2369792

Here, above, we have the same error rate. However, when I try a new prediction with the explicit initial x values, the result is different:

pred2 <- predict(model, newdata = x, type = "class")
table(pred2, y)
1-mean(pred2==y)

     y
pred2   0   1
    0 500   0
    1   0 268
[1] 0

I've already tried the same procedure with the glm function, and in both cases, the result were the same. What is the difference between the two predictions above?

The way you used the formula argument in the RF model is weird, `randomForest(y ~ ., x)` y is a vector and all the predictors are in x. The formula argument expects all variables in the same data frame when you provide a formula. Use `randomForest(y=y,x=x)`. — user2974951, Jul 30 '19 at 13:47
Can you post a chunk of your data or a reproducible example? — user2974951, Jul 31 '19 at 09:23

score 1 · Accepted Answer · answered Jul 31 '19 at 09:38

In the first case (predict(model, type='class') we look at OOB errors and in the second case (predict(model, type='class', data = x) the training data are treated as new data. This second option will artificially inflate the goodness-of-fit from the RF model. CV.SE has very relevant thread under: What measure of training error to report for Random Forests? I would suggest reading through it .

Prediction problem in randomForest in R

1 Answers1