Support Vector Machine

Support Vector Machine

Support Vector Machine is located under Textual Analysis (   ) in Classification, in the left task pane. Use the drag-and-drop method to use the algorithm in the canvas. Click the algorithm to view and select different properties for analysis.

Refer to Properties of Support Vector Machine.


Properties of Support Vector Machine

The available properties of the Support Vector Machine are as shown in the figure below.



The table below describes the different fields present on the properties of the Support Vector Machine.

Field

Description

Remark

RunIt allows you to run the node.-
ExploreIt allows you to explore the successfully executed node.-
Vertical Ellipses

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 name as required.

Dependent Variable

It allows you to select a feature as the dependent variable.

You can select any one feature as the dependent variable.

Text

It allows you to select independent variables.

You can select more than one variable.

Advanced

Penalty Parameter

It allows you to select the value introduced as a penalty to overcome the overfitting problem.

You can select any float value as a penalty parameter.

Kernel

It allows you to select the kernel function to convert your data into a required form.

  • You can select any one of the following kernel functions:
    • linear
    • poly
    • rbf
    • sigmoid
  • By default, the rbf function is selected.

Gamma

It allows you to select the gamma parameter.

  • Model behavior is extremely sensitive to the gamma parameter.
  • Very small gamma values indicate that the model is constrained (smooth) and cannot capture the data complexity.
  • You can select any one of the following gamma parameters:
    • scale
    • auto
  • By default, the scale parameter is selected.

Degree

It allows you to select the degree of the polynomial kernel function.

It is applicable only when you select the poly as the kernel function.

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.

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.

Example of Support Vector Machine

Consider a dataset containing BBC news snippets classified into different categories like tech, business, sport, politics, and so on. A snippet of input data is shown in the figure given below.

We select the following properties and apply Support Vector Machine.

Dependent Variable

Category

Text

Text

Penalty Parameter

1.0

Kernel

Sigmoid

Gamma

Auto

Degree

—

Random State

—

After using the Support Vector Machine, the following results are displayed. On the result page, you can see the following output parameters

  • Accuracy, F-score, Sensitivity, and Specificity are the model performance indicators displayed on the Results page.

  • The Accuracy of the above SVM model is 0.9982, and F-score, Sensitivity, and Specificity are 1 each.

On the same result page, you see the confusion matrix containing predicted and actual values for Category.

  • The shaded diagonal cells show the correctly predicted categories. For example, all 140 pieces of news in the business category are correctly predicted.
  • The remaining cells indicate the wrongly predicted categories. For example, one (1) news in the entertainment category is wrongly predicted as belonging to the politics

The figure below shows the resultant dataset in the Data tab.

  • Along with the Index and Text columns, you can see the Category column showing the original categories.
  • The predicted categories for each text are listed under the Label


  • You can compare Label and Category columns to check them correctly and wrongly predicted categories.

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