In this blog post we present the available learning to rank Apache Solr features with a focus on categorical features and how to manage them.
What is the most appropriate approach to handle queries when splitting data when evaluating learning to rank models?
Does removing constant features affect model performance? Find out with our real-world Learning to Rank application
Query-level features and under-sampled queries, how to handle them? Find it out, with our new Learning to Rank implementations
This blog post aims to illustrate how to generate the query Id and how to manage the creation of the Training Set
How to list, upload, delete feature stores and models necessary in Solr for learning to rank.
This blog post is about several analysis on a LTR model and its explanation using the open source library SHAP
This blog post aims to illustrate step by step a Learning to Rank project on a Daily Song Ranking problem using open source libraries.
Interleaving is an online evaluation approach for ranking functions, contributed to Apache Solr Learning to Rank by Sease.
Introduction A common problem with machine learning models is their interpretability and explainability.We create a dataset and we train a model to achieve a task, then we would like to understand how the model obtains those results. This is often quite difficult to understand, especially with very complex models. In this blog post, I would…