Classification Decision Forest
Decision forest classifier is a special case of the Decision Forest model.
Details
Given:
 nfeature vectors of sizep;
 their nonnegative sample weights ;
 the vector of class labels that describes the class to which the feature vector belongs, where andCis the number of classes.
The problem is to build a decision forest classifier.
Training Stage
Decision forest classifier follows the algorithmic framework of
decision forest training with Gini impurity metrics as impurity
metrics [Breiman84].
If sample weights are provided as input, the library uses a weighted version of the algorithm.
Gini index is an impurity metric, calculated as follows:
where
 Dis a set of observations that reach the node;
 is specified in the table below:
Without sample weights  With sample weights 

is the observed fraction of observations that belong to class i in D 
is the observed weighted fraction of observations that belong to class i in D : 
Prediction Stage
Given decision forest classifier and vectors
,
the problem is to calculate the labels for those
vectors. To solve the problem for each given query vector
,
the algorithm finds the leaf node in a tree in the
forest that gives the classification response by that tree. The
forest chooses the label y taking the majority of trees in the
forest voting for that label.
Outofbag Error
Decision forest classifier follows the algorithmic framework for
calculating the decision forest outofbag (OOB) error, where
aggregation of the outofbag predictions in all trees and
calculation of the OOB error of the decision forest is done as
follows:
 For each vector in the datasetX, predict its label by having the majority of votes from the trees that contain in their OOB set, and vote for that label.
 Calculate the OOB error of the decision forestTas the average of misclassifications:
 If OOB error value per each observation is required, then calculate the prediction error for :
Variable Importance
The library computes
Mean Decrease Impurity
(MDI) importance
measure, also known as the Gini importance
or Mean Decrease
Gini
, by using the Gini index as impurity metrics.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 thecreateTreemethod with the required number of nodes in a tree and a label of the class for which the tree is created.
 Use theaddSplitNodeandaddLeafNodemethods 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 thegetModelmethod to get the trained Decision Forest Classification model after all trees have been created.
Each tree consists of internal nodes (called nonleaf 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 Decision Tree.The inducted decision tree is a binary tree, meaning that each nonleaf 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 Decision Tree.Add nodes to the created tree in accordance with the precalculated structure of the tree.
Check that the leaf nodes do not have children nodes and that the splits have exactly two children.
Examples
Java*
There is no support for Java on GPU.
Batch Processing
Decision forest classification follows the general workflow described in Decision Forest and Classification Usage Model.
Training
In addition to the parameters of a classifier (see Classification Usage Model) and decision forest parameters
described in Batch Processing, the training algorithm for decision forest classification has the
following parameters:
Parameter  Default Value  Description 

algorithmFPType  float  The floatingpoint type that the algorithm uses for intermediate computations. Can be float or double . 
method  defaultDense  The computation method used by the decision forest classification. For CPU:
For GPU: 
nClasses  Not applicable  The number of classes. A required parameter. 
Output
Decision forest classification calculates the result of regression
and decision forest. For more details, refer to Batch Processing and Classification Usage Model.
Prediction
For the description of the input and output, refer to Classification Usage Model.
In addition to the parameters of a classifier, decision forest
classification has the following parameters at the prediction stage:
Parameter  Default Value  Description 

algorithmFPType  float  The floatingpoint type that the algorithm uses for intermediate computations. Can be float or double . 
method  defaultDense  The computation method used by the decision forest classification. The
only prediction method supported so far is the default dense method. 
nClasses  Not applicable  The number of classes. A required parameter. 
votingMethod  weighted  A flag that specifies which method is used to compute probabilities and class labels:

Examples
oneAPI DPC++
Batch Processing:
oneAPI C++
Batch Processing:
C++ (CPU)
Batch Processing:
Java*
Python*