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BayesiaLabPrevious ReleasesBayesiaLab 5.0.2Tools (5.0.2)Cross Validation Structural Coefficient Analysis (5.0.2)

The content of this page refers to an obsolete version of BayesiaLab. Please see the up-to-date entry for Structural Coefficient Analysis .

If both Learning and Test Sets are available, a Validation Measure can be computed to help chose the most appropriate Structural Coefficient.

This measure is based on the Test Set’s mean negative log-likelihood (returned by the network learned from the Learning Set) and on the variances of the negative log-likelihood of the Test Set and Learning Set (returned by the network learned from Learning Set).

γ=μLL,test×max⁡(1,σLL,test2σLL,learning2)\gamma = \mu_{LL,\text{test}} \times \max\left(1, \frac{\sigma_{LL,\text{test}}^2}{\sigma_{LL,\text{learning}}^2}\right)

The “Bathtub”

The bottom portion of the “bathtub” suggests a range of Structural Coefficients for models that do neither overfit nor underfit the data.

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Tools (5.0.2)Temporalization (5.0.2)