What does a seed do in a random forest?

Question

I know that the seed is established at all, used so that we can reproduce the same result. But, what makes the creation of the seed actually in a random part of the forest. Will it change any of the arguments to the randomForest() function in R as nTree or sampSize .

I use different seeds for my random forest model every time, but I want to know how different seeds affect the random forest model.

Answer 1

Trees grow from seeds as well as forests ;-) (scnr)

There are different ways to create a random forest, however, all together - this is that several trees are built. To increase the classification accuracy for a single decision tree, the individual trees in a random forest should be different, since you would have the same tree nTree times. This difference is achieved by introducing randomness in tree generation. Accident is affected by the seed, and the most important thing in the seed is that using the same seed should always generate the same result.

How does randomness affect tree assembly? There are several ways. - build a tree for a random subset. This is a subset of the case study for each individual forest tree, and then a tree is created for this subset - at each decision point in the tree, the adoption attribute is randomly selected.

Often these two elements combine.

http://link.springer.com/article/10.1023%2FA%3A1010933404324#page-1