Class Description Generator

Class Description Generator


  • To manage groups of nodes, BayesiaLab offers Classes.
  • Nodes can be added to Classes manually or automatically. For instance, the Variable Clustering function can assign nodes to new Classes representing latent factors. By default, newly-created Classes have generic names, such as [Factor_0], which carries no meaning.
  • Finding suitable descriptions for Classes can be time-consuming.
  • The Class Description function can assist you in finding meaningful summaries of a Class of nodes.

Using Class Description Generator for Groups of Nodes

  • With the Hellixia Class Description Generator, we can quickly find a useful description for a subset of nodes we select.
  • In our example, we have a large number of nodes from an auto buyer satisfaction survey.
  • We are interested in a subset of nodes related to the quality perception of the vehicle interior, i.e.:
    • Interior Colors
    • Quality of Interior Materials
    • Interior Trim & Finish
    • Quality of Seat Materials
  • Select these nodes node of interest.
  • Then select Menu > Hellixia > Class Description.
  • Specify a Context, if applicable.
  • Indicate by ticking the checkboxes where the subject matter is stored, i.e., Node Name, Node Long Name, or Node Comment. Check all that apply.
  • Clicking OK starts generating the Class Description.
  • The chime confirms when the process is complete.
  • Opening the Class Editor shows the Class Description that was generated.
  • Select Graph Contextual Menu > Edit Classes
  • The Description column shows the newly-generated Class Description.

Workflow Illustration

Using the Class Description Generator from within the Class Editor

  • BayesiaLab's Clustering function produces new Factors and associated Classes.
  • So, having a dozen or more new Classes is quite common in this context.
  • By default, the newly-generated Classes have generic and non-informative names, like [Factor_0], [Factor_1], etc.
  • Given that the Factors and Classes are meant to represent meaningful concepts, naming them is important but can be tedious.
  • In the following example, 57 Factors (and Classes) were created from 240 manifest nodes. Each manifest node measures the degree of agreement or disagreement with statements in a personality test, such as, "I get angry easily" or "I remain calm under pressure."
  • These original statements are included as Node Comments with every node.

Workflow Illustration

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