Overview

The Processor is used for multiclass classification. The Multiclass Linear Support Vector Machine is a standard method for large-scale classification tasks of several classes based on the One-Versus-One method.

Input

The processor requires two input datasets. The left input port corresponds to the training dataset (this data should be already labeled). The right input port corresponds to the test dataset.

The training and the test datasets must have the same schema.

Configuration

Output

The processor returns the input test dataset with additional columns: A column containing the forecasted values and a column for each class containing the One-Versus-One method results.

Example

In this example, the iris flower dataset is used as input. Each row of the dataset represents an iris flower, including its species and dimensions of its botanical parts, sepal and petal, in centimeters. The goal here is to predict the flower's species using the available dimensions.

Example Input

Workflow

The dataset is split into a training and test dataset using a Horizontal Split processor.

Example Configuration

Result

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