A cross-validation package driving Netica with python
Environmental Modelling and Software
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Abstract
Bayesian networks (BNs) are powerful tools for probabilistically simulating natural systems and emulating process models. Cross validation is a technique to avoid overfitting resulting from overly complex BNs. Overfitting reduces predictive skill. Cross-validation for BNs is known but rarely implemented due partly to a lack of software tools designed to work with available BN packages. CVNetica is open-source, written in Python, and extends the Netica software package to perform cross-validation and read, rebuild, and learn BNs from data. Insights gained from cross-validation and implications on prediction versus description are illustrated with: a data-driven oceanographic application; and a model-emulation application. These examples show that overfitting occurs when BNs become more complex than allowed by supporting data and overfitting incurs computational costs as well as causing a reduction in prediction skill. CVNetica evaluates overfitting using several complexity metrics (we used level of discretization) and its impact on performance metrics (we used skill).
| Publication type | Article |
|---|---|
| Publication Subtype | Journal Article |
| Title | A cross-validation package driving Netica with python |
| Series title | Environmental Modelling and Software |
| DOI | 10.1016/j.envsoft.2014.09.007 |
| Volume | 63 |
| Year Published | 2014 |
| Language | English |
| Publisher | Elsevier |
| Contributing office(s) | Wisconsin Water Science Center |
| Description | 10 p. |
| Larger Work Type | Article |
| Larger Work Subtype | Journal Article |
| Larger Work Title | Environmental Modelling and Software |
| First page | 14 |
| Last page | 23 |
| Google Analytic Metrics | Metrics page |