This blog post is about the Apache Solr Learning to Rank Tools : a set of tools to ease the utilisation of the Apache Solr Learning To Rank integration.
The model has been trained in
Part 2, we are ready to deploy it to Solr, but first it would be useful to have a better understanding of what we just created.
A LambdaMART model in a real world scenario is a massive ensemble of regression trees, not the most readable structure for a human.
More we understand the model, easier will be to find anomalies and to fix/improve it.
But the most important benefit of having a clearer picture of the training set and the model is the fact that it can dramatically improves the communication with the business layer :
- What are the most important features in our domain ?
- What kind of document should score high according to the model ?
- Why this document (feature vector) is scoring that high ?
These are only examples, but a lot of similar questions can rise, and we need the tools to answer.