mbok/elasticsearch-linear-regression
A machine learning plugin for Elasticsearch providing aggregations to compute multiple linear regression on search results in real-time for predictive analytics.
This tool helps you predict a numerical outcome, like house prices, based on several influencing factors directly within your Elasticsearch data. You provide your existing data with various features (e.g., house size, bedrooms) and a target outcome (e.g., price), and it outputs a predicted value for new inputs or statistical insights into the relationships. It's designed for data analysts, business intelligence users, or anyone needing real-time predictive insights from their structured data in Elasticsearch.
No commits in the last 6 months.
Use this if you need to quickly estimate a numeric target variable or understand the statistical relationship between variables stored in Elasticsearch, without moving your data to a separate analytical tool.
Not ideal if your prediction problem requires complex machine learning models beyond linear relationships, or if your data is not already indexed in Elasticsearch.
Stars
65
Forks
21
Language
Java
License
Apache-2.0
Category
Last pushed
Oct 07, 2018
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/mbok/elasticsearch-linear-regression"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
o19s/elasticsearch-learning-to-rank
Plugin to integrate Learning to Rank (aka machine learning for better relevance) with Elasticsearch
oracle/tribuo
Tribuo - A Java machine learning library
Waikato/meka
Multi-label classifiers and evaluation procedures using the Weka machine learning framework.
Waikato/moa
MOA is an open source framework for Big Data stream mining. It includes a collection of machine...
allegro/allRank
allRank is a framework for training learning-to-rank neural models based on PyTorch.