How to do it...
In the following example, we will work with the abalone dataset (hosted by the UCI Machine Learning Repository), which contains 4,176 measurements taken for shellfish (sex, length, diameter, height, the number of rings, and several weight metrics). The number of rings is the most important variable, as it determines the age of each specimen. But counting the number of rings is a tedious and costly process, since it needs to be done by cutting each specimen and using a microscope. In general, predictive models are built to predict the number of rings in terms of all the other variables.
In our case, we will formulate a BN model where all the variables are interconnected. We will assume that the sex determines the diameter, ...
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