Difference of prediction results in random forest model

Question

I have built an Random Forest model and I got two different prediction results when I wrote two different lines of code in order to generate the prediction. I wonder which one is the right one. Here is my example dataframe and the usedcode:

dat <- read.table(text = " cats birds    wolfs     snakes
      0        3        9         7
      1        3        8         4
      1        1        2         8
      0        1        2         3
      0        1        8         3
      1        6        1         2
      0        6        7         1
      1        6        1         5
      0        5        9         7
      1        3        8         7
      1        4        2         7
      0        1        2         3
      0        7        6         3
      1        6        1         1
      0        6        3         9
      1        6        1         1   ",header = TRUE)

I've built a random forest model:

model<-randomForest(snakes~cats+birds+wolfs,data=dat,ntree=20)
RF_pred<- data.frame(predict(model))
train<-cbind(train,RF_pred) # this gave me a predictive results named: "predict.model."

I tryed another syntax out of curiosity with this line of code:

dat$RF_pred<-predict(model,newdata=dat,type='response') # this gave me a predictive results named: "RF_pred"

to my suprise I got other predictive results:

 dat
   cats birds wolfs snakes predict.model.  RF_pred
1     0     3     9      7       3.513889 5.400675
2     1     3     8      4       5.570000 5.295417
3     1     1     2      8       3.928571 5.092917
4     0     1     2      3       4.925893 4.208452
5     0     1     8      3       4.583333 4.014008
6     1     6     1      2       3.766667 2.943750
7     0     6     7      1       5.486806 4.061508
8     1     6     1      5       3.098148 2.943750
9     0     5     9      7       4.575397 5.675675
10    1     3     8      7       4.729167 5.295417
11    1     4     2      7       4.416667 5.567917
12    0     1     2      3       4.222619 4.208452
13    0     7     6      3       6.125714 4.036508
14    1     6     1      1       3.695833 2.943750
15    0     6     3      9       4.115079 5.178175
16    1     6     1      1       3.595238 2.943750

Why Is there a diff. between the two? Which one is the correct one? Any Ideas?

Answer 1

The difference is in the two calls to predict:

predict(model)

and

predict(model, newdata=dat)

The first option gets the out-of-bag predictions on your training data from the random forest. This is generally what you want, when comparing predicted values to actuals.

The second treats your training data as if it was a new dataset, and runs the observations down each tree. This will result in an artificially close correlation between the predictions and the actuals, since the RF algorithm generally doesn't prune the individual trees, relying instead on the ensemble of trees to control overfitting. So don't do this if you want to get predictions on the training data.