Summary, Main Concepts, and Takeaways
Explore essential concepts of Bayesian networks including data preprocessing, discretization, learning algorithms, and performance evaluation. Learn to interpret ROC curves to assess model accuracy and improve classification.
We'll cover the following...
Let's revisit the key data-based hyperparameters we discussed in this section:
Preprocessing the data
Data structure before learning: The way you preprocess, organize, and select relevant variables can significantly impact the Bayesian network's performance. This includes handling missing data and transforming variables as needed.
Discretization: Converting continuous variables into discrete variables by dividing their range into discrete bins or categories. The choice of discretization method and number of bins can affect the BN's complexity and performance. Techniques such as equal-width binning, equal-frequency binning, and supervised discretization methods are commonly used.
Algorithms for Bayesian networks
Search strategy: The approach used to explore the space of possible ...