Target Node and Target State

Context

In BayesiaLab, Supervised Learning includes a set of algorithms that can learn Bayesian networks that have a structure entirely focused on characterizing a Target Node.
In other words, the Target Node is the equivalent of what would be considered a dependent variable in traditional modeling approaches.

The Target Node is visually distinguished from a normal node by its "bullseye" styling .
Additionally, the Monitor that corresponds to the Target Node features a green background.

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With Unsupervised Learning, having a Target Node in the network has no influence on the learning algorithm.

There are several ways of defining a node as a Target Node:

You can define a node as a Target Node by right-clicking on it and selecting Set as Target Node from the Node Context Menu. Selecting the same item from the menu reverts the node to its original role.
Holding T and double-clicking on a node turns it into a Target Node. Holding T and double-clicking on a Target Node makes it a normal node again.

You can define a node as a Target Node by right-clicking on it and selecting Set as Target Node from the Node Context Menu. You then have to choose the Target State via the drop-down list.
Holding T and double-clicking on a node turns it into a Target Node.
You can also define the target via Monitor:
- Bring up the Monitor of the desired node in the Monitor Panel.
- Then, while holding T, double-click into the bar chart area of that Monitor.
- This sets the Target Node and the Target State at the same time.

If there is already a Target Node in the network, assigning a new Target Node automatically reverts the previous Target Node to its default status.

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At any given time, there can only be one Target Node in a network.

Whenever you have a Target Node, there is automatically a Target State.
By default, the first state of a node is set as the Target State of a Target Node.
This Target State is marked with a bullseye symbol on the Monitor of the Target Node.
While a Target State is present whenever there is a Target Node, the Target State does not affect the learning algorithms in any way.
As such, you can disregard the Target State for all modeling purposes.
In fact, the Target State is neither visible nor accessible in Modeling Mode F4.
However, for some analysis purposes in the Validation Mode F5, it can be useful to consider a Target State.
For instance, a Target State can be used for evaluating a model with regard to predicting one particular state.
In many applications, only one particular state is of relevance. For example, a Target Node may represent multiple virus types, e.g., Dengue, Zika, Rhinovirus, Influenza, and Coronavirus but, perhaps, only the model's performance for diagnosing the Target State Coronavirus is critical.

For structural learning, it is important which node is the Target Node but it does not matter what the Target State of the Target Node is.

Given that it is irrelevant for modeling purposes, the Target State is only visible and modifiable in Validation Mode F5.
In Validation Mode, there are multiple ways you can set the Target State to the state of interest:

Option 1:
- Right-click on the Target Node and select Set as Target Node from the Node Context Menu again.
- This brings up a Target Selection window, from which you can choose the Target State.
Option 2:
- Bring up the Monitor corresponding to the Target Node.
- Then, while holding T, double-click into the bar chart area of the desired state.

Option 1:
- Right-click on the desired node and select Set as Target Node from the Node Context Menu.
- Then, check the Target Node box select the desired Target State from the drop-down list.
Option 2:
- Bring up the Monitor of the desired node in the Monitor Panel.
- Then, while holding T, double-click into the bar chart area of the desired state.
- This sets the Target Node and the Target State at the same time.