Learning
Context
The Learning menu provides access to a wide range of learning algorithms and related functions:
The following functions make up the core of the Learning menu and are central to BayesiaLab’s machine-learning capabilities:
- Unsupervised Structural Learning to discover high-dimensional probabilistic relationships in data.
- Supervised Learning to develop predictive models focused on a Target Node.
- Clustering to identify latent concepts among nodes and within datasets.
All learning algorithms employ the Minimum Description Length Score in the search for the best network among all candidate networks. In this context, the Minimum Description Length Score takes into account both the fit of the Bayesian network to the dataset and the structural complexity of the network structure. By minimizing the Minimum Description Length Score, the learning algorithm optimizes the trade-off between fit and complexity. In the Console, you can observe the evolution of the MDL Score as a result of using different learning algorithms. Also, for an existing network, you can obtain its Minimum Description Length Score.