Rediscretize Continuous Nodes

Context

• Rediscretize Continuous Nodes is an option when setting up a Structural Coefficient Analysis.
• When Rediscretize Continuous Nodes is enabled, it performs automatic rediscretization at each iteration (each Structural Coefficient value) for nodes that were originally discretized with an automatic discretization algorithm.
• Nodes that were discretized manually are not included in the rediscretization. Such nodes keep their original intervals across all iterations.
• Rediscretize Continuous Nodes uses the originally selected discretization algorithm and the originally selected number of intervals.

Tree-Based Discretization Algorithms

• Rediscretize Continuous Nodes is particularly useful in the context of Supervised Learning when used in conjunction with Tree-based discretization algorithms.
• Tree-based discretization algorithms utilize the Minimum Description Length Score, which depends directly on the Structural Coefficient.
• As a result, the discretization thresholds are optimized for each value of the Structural Coefficient.
• This approach can help you fine-tune a model by trading off fit and complexity.
• Note that the Target Node in a network is never rediscretized automatically in this context.

Stochastic Discretization Algorithms

• If a Fixed Seed is set for the Random Number Generator under Main Menu > Window > Preferences > General > Random Number Generator, the following stochastic discretization algorithms will always produce identical discretizations each time they run:
  • R2-GenOpt,
  • K-Means,
  • Supervised Multivariate,
  • Unsupervised Multivariate.
• If a Fixed Seed is not set, Rediscretize Continuous Nodes will use the stochastic nature of the above discretization algorithms and produce stochastic variations in the discretization results.