Associated Namespace: IMAGINE
The operator defines and trains a Random Forest classifier which is used as an input for classifying data using the Classify Using Machine Intellect operator. All non-geometry attribute fields in the TrainingData (except for ClassAttributeName) will be used for training. The Select Attributes or Remove Attributes operator can be used to tailor the training data's attribute schema before the training data is used in the Initialize Random Forest operator.
Random Forest is an ensemble of decision trees and one of the most popular machine learning algorithms. In Random Forest, we use multiple trees generated with a different selection of the training samples as opposed to a single tree in CART. Each tree in the forest gives a vote and an object is assigned/classified to the class that has the most votes.
Raster to be orthorectified
The attribute field from TrainingData which defines the classes into which the resulting MachineIntellect may classify features.
Seed for the random number generator that is used during the training. Fixing the seed at a specific value allows repeated executions of the operator with the same input data to produce the exact same results.
Empty. The trained Random Forest classifier is random. That is, running this operator multiple times will generate (slightly) different results.
The trained Random Forest classifier