8) Test Classification Accuracy of a Bayesian Network.

This Blog entry is from the Netica Basics section in Learn Netica.

Bayesian Networks are viewed to be extremely useful for classification problems with the measure of the performance of being classification accuracy, commonly presented as a confusion matrix (in the same manner as Logistic Regression).

Bayesian networks, once constructed and trained, can facilitate a testing process which produces similar analysis to that observed in logistic regression Blog entries.

Firstly, highlight all nodes required by holding down the ctrl key and clicking the node name:

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Bayesian networks, once constructed and trained, can facilitate a testing process which produces similar analysis to that observed in logistic regression Blog entries.

Firstly, highlight all nodes required by holding down the ctrl key and clicking the node name:

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To test the network, navigate to the Cases menu, then click on the Test with Cases sub menu:

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Select the CreditRisk.csv file when prompted to open a file:

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Clicking the Open button begins the testing process, for the dependent variable, this is Default in this example, a Confusion Matrix and Error Rate is presented, being the main focus of optimisation in a stepwise approach, or perhaps using more automated means to add nodes to the canvas and establish relationships between the independent variables:

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