Random forests are an ensemble learning method for classification, regression and other tasks, that operate by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees. Random decision forests correct for decision trees’ habit of overfitting to their training set.
Random Forest builds many trees using a subset of the available input variables and their values, it inherently contains some underlying decision trees that omit the noise generating variable/feature(s).