Initialize Random Forest

ERDAS IMAGINE Help

HGD_Variant
16.5
HGD_Product
ERDAS IMAGINE
HGD_Portfolio_Suite
Producer

Category: Classification

Associated Namespace: IMAGINE

Default

Show All Ports

InitializeRF_Default

InitializeRF_AllPorts.PNG

Description

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.

Limitations

None

Connections

Name

Objects Supported

Description

Shown by Default

Required

Default Behavior or Behavior if not Required

TrainingData

IMAGINE.Features

Raster to be orthorectified

checkmark_gray

checkmark_gray

ClassAttributeName

IMAGINE.String

The attribute field from TrainingData which defines the classes into which the resulting MachineIntellect may classify features.

checkmark_gray

checkmark_gray

RandomSeed

IMAGINE.Unsigned

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.

MachineIntellect

IMAGINE.MachineIntellect

The trained Random Forest classifier

checkmark_gray

Related Operators

Initialize CART, Initialize K-Nearest Neighbors, Initialize Naive Bayes, Initialize SVM, Classify Using Machine Intellect

Example Model

InitializeRandomForest_example