The available options are

• Run till node
• Run from node
• Publish as a model
• Publish code

Task Name

It is the name of the task selected on the workbook canvas.

You can click the text field to edit or modify the task's name as required.

Dependent Variable

It allows you to select the dependent variable.

You can select one variable.

Text

It allows you to select the text variable.

The default value is eight (8).

Advanced

Random State

It allows you to select a random combination of train and test for the classifier.

• It randomizes the data splitting into train and test. This provides different data each time for training and testing the model.
• The model is expected to return the same result each time, even with different train-test combinations.
• It ensures that the obtained results can be reproduced.
• You can enter any integral value as the random state
• This parameter is optional.

Learning Rate

It allows you to select the weight value for the learning rate of weak learning algorithms.

• By default, the value for the learning rate is 0.1.
• You can enter any float value between 0 and 1.
• A low learning rate indicates that the contribution by each learner is less. In that case, the number of estimators should be kept high.

Number of Estimators

It allows you to select the number of weak learners to be created while running the algorithm.

• By default, the number of estimators selected is 50.
• You can enter any integral number of estimators.
• The number of estimators should be decided based on the selected Learning Rate.

Algorithm

It allows you to select the boosting algorithms for classification.

There are two types of boosting algorithms.

·        Discrete boosting algorithm

·        Real boosting algorithm

• Algorithm selection is important, especially when you have multi-class datasets.
• You can select any one of the following algorithms:
  • SAMME (discrete)
  • SAMME.R (real)
• SAMME considers the discrete values while SAMME.R considers the probability values.
• Usually, SAMME.R converges faster and creates low test errors with few boosting iterations.
• By default, the SAMME.R algorithm is used.

Add result as a variable

It allows you to select any of the result parameters as the variable.

You can select from the following performance parameters of SVM:

• Accuracy
• Sensitivity
• Specificity
• F-score

Node Configuration

It allows you to select the instance of the AWS server to provide control on the execution of a task in a workbook or workflow.

For more details, refer to Worker Node Configuration.

Hyperparameter Optimization

It allows you to select parameters for optimization.

For more details, refer to Hyperparameter Optimization.