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.
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.
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.
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.
The dataset is split into a training and test dataset using a Horizontal Split processor.
Decision Tree Regression Forecast
Decision Tree Classification Forecast
Random Forest Classification Forecast Processor
Random Forest Regression Forecast Processor