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
How to manage large JSON efficiently and quickly: multiple files
Tips and tricks to find out efficient and fast ways to read and parse a big JSON file in Python using real-world application
Ilaria Petreti
November 25, 2021
How to manage large JSON files in Python efficiently and quickly
Tips and tricks to find out efficient and fast ways to read and parse a big JSON file in Python using real-world application
Ilaria Petreti
October 13, 2021
Drop constant features: a real-world Learning To Rank scenario
Does removing constant features affect model performance? Find out with our real-world Learning to Rank application
Ilaria Petreti
June 16, 2021
A Learning to Rank Project on a Daily Song Ranking Problem – Part 4
Query-level features and under-sampled queries, how to handle them? Find it out, with our new Learning to Rank implementations
Ilaria Petreti
May 31, 2021
A Learning to Rank Project on a Daily Song Ranking Problem – Part 3
This blog post aims to illustrate how to generate the query Id and how to manage the creation of the Training Set
Ilaria Petreti
March 25, 2021
A Learning to Rank Project on a Daily Song Ranking Problem – Part 2
This blog post is about several analysis on a LTR model and its explanation using the open source library SHAP
Ilaria Petreti
February 15, 2021
A Learning to Rank Project on a Daily Song Ranking Problem
This blog post aims to illustrate step by step a Learning to Rank project on a Daily Song Ranking problem using open source libraries.
Ilaria Petreti
December 18, 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.
