Hyperparameters to Train a Bayesian Network
Understand how to set and optimize structural and data-driven hyperparameters essential for constructing and training accurate Bayesian networks. Learn the impact of network structure, data preprocessing, learning algorithms, and score functions on model performance to build effective causal models.
We'll cover the following...
In this lesson, we explore the essential structural criteria to consider when constructing causal models based on Bayesian networks. Our primary focus will be on the model's structure and the concept of hyperparameters, which are expert-dependent parameters that need to be set before running the learning algorithms.
For instance, hyperparameters are adjustable, high-level parameters that govern the behavior of the learning process and the overall architecture of the model. They are typically set before training the model and are not learned during the training process. The choice of hyperparameters can significantly impact the model's performance.
Hyperparameters in Bayesian networks
Hyperparameters rely on the available data as well as expert knowledge. As a result, the challenge extends beyond parameter optimization and encompasses the setting of hyperparameters. Selecting the optimal configuration of hyperparameters in BNs can be a complex task, as these parameters are grounded in the model's semantics. It is crucial that the chosen hyperparameters are pertinent to the data used for training the model and the expert's knowledge. Some common hyperparameters in BNs include:
Structure-based hyperparameters ...