Tools | Multi-Run | Structural Coefficient Analysis
This tool helps choosing the best Structural Coefficient by testing structural learning algorithms with a range of coefficients, impacting thus the structural complexity of the machine learned networks.
Renamed Menu Item
This feature was previously under Tools | Cross-Validation.
New Feature: New Metrics
As of version 7.0, two additional metrics are available for evaluating the impact of the coefficients on the quality of the induced networks:
- The Contingency Table Fit, for measuring the representation quality of the data (right part of the MDL score, )
- The Degree of Freedom Reduction Efficiency (
It is defined as follow:
is the degree of freedom of the fully connected network F,
is the degree of freedom of the current network B,
and is the Contingency Table Fit of the current network B. ), a new metric for measuring how the complexity of the model impacts the representation quality of the data.
New Feature: Rediscretize Continuous Nodes
This new option allows running automatic discretization before executing the selected structural learning algorithm with the current Structural Coefficient.
The discretization is only run for the continuous variables that have an associated automatic discretization algorithm, i.e. for which the discretization thresholds have not been manually defined (or modified). Note the Target Node is never rediscretized in this context.
The main purpose of this option is to allow testing the impact of the Structural Coefficient on the discretization algorithms. It is thus geared toward supervised learning problems, where the variables are discretized with Tree based approaches. The Structural Coefficient is indeed also used in the MDL score that is utilized for the induction of the trees.
However, when the Seed is not fixed, this can also have an impact on the following discretization algorithms that are stochastic by nature:
Updated Feature: Structure Comparison
Comparing the structure of the learned networks usually helps deciding which coefficient to finally utilized. The networks are now stored from the largest Structural Coefficient to the smallest.