Developer Guide

Contents

Usage of Training Alternative

To build a Decision Forest Classification model using methods of the Model Builder class of Decision Forest Classification, complete the following steps
  • Create a Decision Forest Classification model builder using a constructor with the required number of classes and trees.
  • Create a decision tree and add nodes to it:
    • Use the
      createTree
      method with the required number of nodes in a tree and a label of the class for which the tree is created.
    • Use the
      addSplitNode
      and
      addLeafNode
      methods to add split and leaf nodes to the created tree. See the note below describing the decision tree structure.
    • After you add all nodes to the current tree, proceed to creating the next one in the same way.
  • Use the
    getModel
    method to get the trained Decision Forest Classification model after all trees have been created.
Each tree consists of internal nodes (called
non-leaf
or
split
nodes) and external nodes (leaf nodes). Each split node denotes a feature test that is a Boolean expression, for example,
f
<
featureValue
or
f
=
featureValue
, where
f
is a feature and
featureValue
is a constant. The test type depends on the feature type:
continuous
,
categorical
, or
ordinal
. For more information on the test types, see Algorithms > Training and Prediction > Classification and Regression > Decision Tree > Details.
The inducted decision tree is a binary tree, meaning that each non-leaf node has exactly two branches: true and false. Each split node contains
featureIndex
, the index of the feature used for the feature test in this node, and
featureValue
, the constant for the Boolean expression in the test. Each leaf node contains a
classLabel
, the predicted class for this leaf. For more information on decision trees, see Algorithms > Training and Prediction > Classification and Regression > Decision Tree.
Add nodes to the created tree in accordance with the pre-calculated structure of the tree. Check that the leaf nodes do not have children nodes and that the splits have exactly two children.

Product and Performance Information

1

Intel's compilers may or may not optimize to the same degree for non-Intel microprocessors for optimizations that are not unique to Intel microprocessors. These optimizations include SSE2, SSE3, and SSSE3 instruction sets and other optimizations. Intel does not guarantee the availability, functionality, or effectiveness of any optimization on microprocessors not manufactured by Intel. Microprocessor-dependent optimizations in this product are intended for use with Intel microprocessors. Certain optimizations not specific to Intel microarchitecture are reserved for Intel microprocessors. Please refer to the applicable product User and Reference Guides for more information regarding the specific instruction sets covered by this notice.

Notice revision #20110804