# Polynomial by Binomial Classification (AI Studio Core)

## Synopsis

This operator builds a polynomial classification model through the given binomial classification learner.## Description

The Polynomial by Binomial Classification operator is a nested operator i.e. it has a subprocess. The subprocess must have a binomial classification learner i.e. an operator that generates a binomial classification model. This operator builds a polynomial classification model using the binomial classification learner provided in its subprocess. You need to have basic understanding of subprocesses in order to apply this operator. Please study the documentation of the Subprocess operator for basic understanding of subprocesses.

Many classification operators (e.g. the SVM operator) allow for classification only for binomial (binary) label. The Polynomial by Binomial Classification operator uses a binomial classifier and generates binomial classification models for different classes and then aggregates the responses of these binomial classification models for classification of polynomial label.

## Input

- training set (Data table)
This input port expects an ExampleSet. It is the output of the Retrieve operator in the attached Example Process. The output of other operators can also be used as input.

## Output

- model (Model)
The polynomial classification model is delivered from this output port. This classification model can now be applied on unseen data sets for prediction of the

*label*attribute. - example set (Data table)
The ExampleSet that was given as input is passed without changing to the output through this port. This is usually used to reuse the same ExampleSet in further operators or to view the ExampleSet in the Results Workspace.

## Parameters

- classification strategiesThis parameter specifies the strategy that should be used for multi-class classifications i.e. polynomial classifications.
- random code multiplicatorThis parameter is only available when the
*classification strategies*parameter is set to 'exhaustive code' or 'random code'. This parameter specifies a multiplicator regulating the codeword length in random code modus. - use local random seedThis parameter indicates if a
*local random seed*should be used for randomization. Using the same value of*local random seed*will produce the same randomization. - local random seedThis parameter specifies the
*local random seed*. This parameter is only available if the*use local random seed*parameter is set to true.

## Tutorial Processes

### Using the SVM operator for polynomial classification

The 'Iris' data set is loaded using the Retrieve operator. The Split Validation operator is applied on it for training and testing a polynomial classification model. The Polynomial by Binomial Classification operator is applied in the training subprocess of the Split Validation operator. The SVM operator is applied in the subprocess of the Polynomial by Binomial Classification operator. Although SVM is a binomial classification learner but it will be used by the Polynomial by Binomial Classification operator to train a polynomial classification model. The Apply Model operator is used in the testing subprocess to apply the model. The resultant labeled ExampleSet is used by the Performance (Classification) operator for measuring the performance of the model. The polynomial classification model and its performance vector is connected to the output and it can be seen in the Results Workspace.