random_forests

##Description This class implements a Random Decision Forests classifier. Random Forests are an ensemble learning method that operate by building a number of decision trees at training time and outputting the class with the majority vote over all the trees in the ensemble.

The Random Forests algorithm is part of the GRT classification modules.

##Advantages The Random Forests algorithm is a good algorithm to use for complex classification tasks. The main advantage of a Random Forests is that the model created can easily be interrupted.

##Disadvantages The main limitation of the Random Forests algorithm is that a large number of trees may make the algorithm slow for real-time prediction.

##Training Data Format You should use the ClassificationData data structure to train the Random Forests classifier.

##Example Code Random Forests