radinhamidi/Hybrid_Forest
Hybrid Forest: A Concept Drift Aware Data Stream Mining Algorithm
Hybrid Forest helps data analysts and machine learning practitioners who work with constantly changing data. It takes in a continuous stream of data and identifies shifts in patterns or relationships within that data over time. The output helps you understand when your predictive models might no longer be accurate due to changes in the underlying data behavior.
No commits in the last 6 months.
Use this if your data streams are dynamic and you need to detect when the statistical properties of your data change, potentially invalidating your existing models.
Not ideal if your data is static or if you only need to build models on historical, unchanging datasets.
Stars
9
Forks
3
Language
Python
License
—
Category
Last pushed
Jan 29, 2019
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/radinhamidi/Hybrid_Forest"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
online-ml/river
🌊 Online machine learning in Python
IFCA-Advanced-Computing/frouros
Frouros: an open-source Python library for drift detection in machine learning systems.
NannyML/nannyml
nannyml: post-deployment data science in python
Western-OC2-Lab/AutoML-Implementation-for-Static-and-Dynamic-Data-Analytics
Implementation/Tutorial of using Automated Machine Learning (AutoML) methods for static/batch...
etsi-ai/etsi-watchdog
Real-time data drift detection and monitoring for machine learning pipelines.