Solved – randomForest same formula different results (with same seed!)

set.seed=500 initializes a variable called set.seed and sets it to 500. It does not set the random number generator seed.

Use set.seed(500) instead.

You can look at the help page by ?set.seed.

In addition, note that your first model (x = training_set) includes all columns of the training data set – including the dependent variable medv. In contrast, the second one (medv ~ .) tells R to exclude the DV from the IVs. Of course these will give different results, since the training data are different.

Below, I give a reproducible example. The last model is an adaptation of your first model, and it indeed gives the same results as your second one.

library(randomForest) library(MASS) training_set <- Boston  set.seed(500) regressor = randomForest(x = training_set,                           y = training_set$medv,                           ntree = 100) regressor  set.seed(500) regressor =randomForest(medv ~ . , data = training_set,ntree=100)  regressor  set.seed(500) regressor = randomForest(x = training_set[,-14],                           y = training_set$medv,                           ntree = 100) regressor 

Finally, note that you will typically get better help if you include a minimal reproducible example like the one I gave here.