mzaradzki/factorization-machine-for-prediction
Factorization Machine for regression and classification
This tool helps data analysts and machine learning practitioners build predictive models, especially when dealing with many features or categorical variables. It takes your dataset as input and generates a model that can predict outcomes (like a score or a category) by efficiently capturing interactions between different input variables. You'd use this if you need accurate predictions from complex data, for tasks such as personalized recommendations or click-through rate prediction.
No commits in the last 6 months.
Use this if your predictive modeling task involves a large number of variables, particularly if many are categorical and you suspect interactions between them are important for accurate predictions.
Not ideal if your dataset has only a few variables and you don't expect complex interactions, as simpler linear models might suffice.
Stars
97
Forks
12
Language
Jupyter Notebook
License
Apache-2.0
Category
Last pushed
Jun 22, 2017
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/mzaradzki/factorization-machine-for-prediction"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
meta-pytorch/torchrec
Pytorch domain library for recommendation systems
recommenders-team/recommenders
Best Practices on Recommendation Systems
hongleizhang/RSPapers
RSTutorials: A Curated List of Must-read Papers on Recommender System.
kakao/buffalo
TOROS Buffalo: A fast and scalable production-ready open source project for recommender systems
RUCAIBox/CRSLab
CRSLab is an open-source toolkit for building Conversational Recommender System (CRS).