Incremental Learning

Incremental Learning is located under Textual Analysis (   ) in Clustering, 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 Incremental Learning.

Properties of Incremental Learning

The available properties of Incremental Learning are shown in the figure given below.

The table given below describes the different fields present on the properties of Incremental Learning.

Field		Description	Remark
Run		It allows you to run the node.	-
Explore		It 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 name of the task as required.
Text		It allows you to select Independent variables.	You can select more than one variable. You can select any type of variable.
Number of Clusters		It allows you to enter the number of clusters you want to create.	The default value is 3.
Advanced	Threshold		The default value is 0.5.
	Branching Factor	It allows you to enter the number of child nodes that can be created at each node.	The default value is 50.

Example of Incremental Learning

Consider a dataset of Musical Instruments review. A snippet of input data is shown in the figure given below.

After using the Incremental Learning, the following results are displayed.

As seen in the above figure, the data is divided into three clusters. A pie chart of the clusters, each cluster's size, and the Silhouette Score are displayed.

• Related Articles

• Incremental Learning

  Incremental Learning is located under Textual Analysis ( ) in Clustering, 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 ...

• Publishing Models

  You can publish algorithms as models after their successful execution. A model can be reused in a workbook for training and experimenting or can be used in a workflow for production. Notes: This functionality is available only for Machine Learning ...

• AutoML

  AutoML is located under Machine Learning > AutoML in the task pane. Use the drag-and-drop method (or double-click on the node) to use the algorithm in the canvas. Click the algorithm to view and select different properties for analysis. Example of ...

• Creating a Workbooka

  To create a workbook, follow the steps given below. On the home page, click the Create icon (). Hover over the Machine Learning tile and click the Create Workbook button. Create Workbook page is displayed. Enter the Name for your workbook. Enter the ...

• Adaboost

  Adaboost 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 ...