Random Forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all the trees in the forest. The generalization error for the forests converges as to a limit as the number of trees in the forest becomes large.This error of tree clarifiers depends on the strength of the individual trees in the forest and the correlation between them. Using a random selection of features split each node yields error rates that compare favorably to Adaboost but are most robust with respect to noise. Internal estimates monitor error,strength,and correlation and these are used to show the response to increasing the number of features used in the splitting. The Internal estimates are also used to measure variable importance.These ideas are also applicable to regression