In machine learning, decision trees are a great algorithm family to work with business information. They are not the most precise nor are they considered cutting edge, but they are a first pass algorithm for many data scientists. Maybe in version two of a project, another algorithm family might create a better model for delivering a reliable model, but over most types of transaction or ERP data, decision trees as a class are where most data scientists start.
One of the great things for business use is that decision trees can be deciphered and understood by people. That capability lends them an air of credibility if managers and executives can look at the logic of the tree and follow how the final answer is made by tracking the tree at each branch.
It also lets IBM i programmers code the decision tree splits in familiar programming languages. Realistically, this is the only way decision trees are going to work with IBM i programs natively on the box without making calls out to other servers.
In reality, you'll want to make those calls out from your programs rather than code the decision tree. There are many reasons that go beyond just the simple work of coding hundreds or even thousands of decision points into a program. The easiest way to explain is to ask the question, “What happens when they change the model?”
It will happen. It always happens.
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