Train and Test Sets Split for Evaluating Learning To Rank Models – Part 2
What is the most appropriate approach to handle queries when splitting data when evaluating learning to rank models?
Ilaria Petreti
September 13, 2022
Train and Test Sets Split for Evaluating Learning To Rank Models
How data splitting can be done and why it is important for the offline evaluation of Learning to Rank models?
Ilaria Petreti
July 20, 2022
How to Deal with Too Many ‘object’ in Pandas from JSON Parsing
We propose and test a way to manage categorical data during the collection and store it directly as numeric types in the JSON.
Ilaria Petreti
March 30, 2022
Common Errors with Apache Solr Feature and Model Stores
Common errors and warnings in manipulating feature stores and models in Solr. Pay attention also to JVM Heap and Zookeeper.
Anna Ruggero
March 8, 2021
Apache Solr Learning to Rank Feature Stores and Models
How to list, upload, delete feature stores and models necessary in Solr for learning to rank.
Anna Ruggero
February 25, 2021
Explaining Learning to Rank Models with Tree Shap
Explainability and Interpretability of Learning To Rank models are vital in Information Retrieval, in this blog we present Tree SHAP.
Anna Ruggero
July 15, 2020
We are Sease, an Information Retrieval Company based in London, focused on providing R&D project guidance and implementation, Search consulting services, Training, and Search solutions using open source software like Apache Lucene/Solr, Elasticsearch, OpenSearch and Vespa.
