Performance Metrics
Improve your Bayesian network's performance and accuracy by learning key data-based performance metrics.
We'll cover the following...
Welcome to this section on tuning data-based hyperparameters of a Bayesian network. In this lesson, we will explore various techniques and approaches to improve the performance and accuracy of our Bayesian network model. The topics we will cover in this section include:
Features optimization: We'll introduce the key data-based performance metrics for Bayesian models and demonstrate how to evaluate the model's performance based on these metrics, particularly using the Receiver Operating Characteristic (ROC) curve.
Discretize data: We'll explore data preprocessing and preparation techniques such as discretization to ensure accurate and suitable data for training and inference in BN models.
AIC and BIC curve: We'll learn to interpret AIC and BIC curves for comparing and evaluating BN structures, helping us choose the best model for our data.
Parameter learning algorithms: We'll cover various algorithms like Maximum Likelihood Estimation (MLE) and Bayesian Estimation for estimating conditional probability distributions in BNs.
Structure learning algorithms: We'll cover ...