2 min read Research

fseval: A Benchmarking Framework for Feature Selection and Feature Ranking Algorithms

Journal of Open Source Software — A Python package for benchmarking Feature Selection and Feature Ranking algorithms at scale: parallel and distributed experiments, fully reproducible from a config file, with results exported to SQL.

By Jeroen G. S. Overschie, Ahmad Alsahaf and George Azzopardi. Published in the Journal of Open Source Software on 23 November 2022 (doi:10.21105/joss.04611).

Code · PDF · Journal

Summary

The fseval Python package allows benchmarking Feature Selection and Feature Ranking algorithms on a large scale, and facilitates the comparison of multiple algorithms in a systematic way. In particular, fseval enables users to run experiments in parallel and distributed over multiple machines, and export the results to an SQL database. The execution of an experiment can be fully determined by a configuration file, which means the experiment results can be reproduced easily, given only the configuration file. fseval has high test coverage, continuous integration, and rich documentation. The package is open source and can be installed through PyPI.

fseval: A Benchmarking Framework for Feature Selection and Feature Ranking Algorithms
Overschie et al., (2022). fseval: A Benchmarking Framework for Feature Selection and Feature Ranking Algorithms. Journal of Open Source Software, 7(79), 4611, https://doi.org/10.21105/joss.04611
Journal of Open Source Software
GitHub - dunnkers/fseval: Benchmarking framework for Feature Selection and Feature Ranking algorithms 🚀
Benchmarking framework for Feature Selection and Feature Ranking algorithms 🚀 - dunnkers/fseval
GitHub

Published by:

Jeroen Overschie

You might also like...

24
Jul
Our internal AI agent for expenses: Bonbonnetje 🍬

Our internal AI agent for expenses: Bonbonnetje 🍬

Bonbonnetje is an AI Agent running in production on Agent Platform helping Xebians submit expense claims. Bonbonnetje is in active use by multiple internal departments and successfully completes hundreds of submissions monthly, helping save both employees and back-office valuable time.
5 min read
03
Oct
Fine-tuning LLMs yourself

Fine-tuning LLMs yourself

Fine-tuning can be a powerful way to specialised Large Language Models (LLMs). With open source models readily available, it is now also possible to fine-tune yourself. Read along to find out how.
7 min read
09
Apr
The GenAI automation potential of data extraction

The GenAI automation potential of data extraction

GenAI has been around for a little while and its capabilities are expanding quickly. But still, many companies fail to generate actual value with GenAI. Why is that? In this blogpost, we explore the GenAI automation potential that exists today for data extraction.
10 min read
27
Sep
The Levels of RAG

The Levels of RAG

LLM’s can be supercharged using a technique called RAG, allowing us to overcome dealbreaker problems like hallucinations or no access to internal data. But what can we expect from RAG? What use cases work well and which are more challenging? Let’s find out together!
15 min read
11
Jul
PyData Eindhoven 2024

PyData Eindhoven 2024

Eindhoven, The Netherlands — Retrieval Augmented Generation (RAG) is powerful. Let's go from Basic (chunking, vector embeddings, nearest-neighbor search) to Hybrid Search (TF-IDF, BM-25 and Reciprocal Rank Fusion) to Advanced (PDF parsing, tables, graphs) and finally, the cutting edge: Multimodal.