Running Decision Trees in Neo4j

Max De Marzi presents how decision trees are used to make near real-time decisions using a graph database, specifically with Neo4j. He uses the example of a nightclub entrance criteria where rules change at the owner's discretion. Max shows how to build a decision tree on top of a graph database using Java API traversal framework, which includes evaluators and expanders. The decision tree takes code as data and runs it in real-time, with up to 10 different rule nodes still delivering the same result performance. He also discusses building stored procedures, the decision path method, and the `isTrue` magical method that evaluates a rule's parameters and returns a Boolean answer. The use case is pharmaceutical drug recommendations where users build a decision tree based on research data, and Max shares his blog and open-source code repositories for further learning.