Target Evaluation

Target Evaluation

BayesiaLab offers numerous tools to evaluate the performance of a network generated by Supervised Learning:

  • Confusion Matrix (Occurrences, Reliability, Precision)
  • Gains Curve
  • Lift Curve
  • ROC Curve
  • R, R2, Relative Gini Index, Relative Lift Index

For datasets with hold-out samples, all metrics are reported separately.

Overall Evaluation

As opposed to the Target Evaluation, the Overall Evaluation function provides an assessment of the fit of the entire network, i.e. with regard to all variables. This is ideally suited for for networks generated with Unsupervised Learning.



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