Ensemble learning is the boosting technique that helps us in improving the accuracy of the prediction. We will also learn how to use the graph database for knowledge storage. Knowledge storage is the current challenge in knowledge representation that can be used to empower AI for professional-grade financial services.
Ensemble learning is an approach that is used to summarize several models in order to give a more stable prediction. It was a very common approach before deep neural networks became popular. For completeness, we do not want to ignore this modeling technique in this very short book. In particular, we have used random forest, which means that we build lots of decision trees as a forest and we apply logic to cut down trees that have lower performance. Another approach would be combining the weaker model to generate a strong result, which is called the boosting method. We won't cover it here, but readers are encouraged to dig deeper in...
