Skip to main content

retrieveStack

Project description

https://travis-ci.org/MacHu-GWU/elementary_math-project.svg?branch=master https://img.shields.io/pypi/v/retrieveStack.svg https://img.shields.io/pypi/l/retrieveStack.svg https://img.shields.io/pypi/pyversions/retrieveStack.svg

Welcome to retrieveStack Documentation

retrieveStack is an analytical framework by resembling a feedforward neural network and using stacked generalization in multiple levels to improve accuracy in classification (or regression) problems. In contrast to conventional feedforward neural-network-like stacked structures, retrieveStack gathers the information of models in all layers, choose good models from multiple layers by comparing to output layer, and combine them with output layer model to optimize final prediction. Below is the pictorial description of how retriveStack working:

https://raw.githubusercontent.com/Robin888/retrieveStack-project/master/desc.jpg

retrieveStack gives better prediction than the best single model contains in first layer. Please note its performance relies on a mix of strong and diverse single models in order to get the best out of this analytical framework

Install

retrieveStack is released on PyPI, so all you need is:

$ pip install retrieveStack

To upgrade to latest version:

$ pip install --upgrade retrieveStack

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for retrieveStack, version 0.0.1
Filename, size File type Python version Upload date Hashes
Filename, size retrieveStack-0.0.1-py2.py3-none-any.whl (7.6 kB) File type Wheel Python version any Upload date Hashes View

Supported by

Pingdom Pingdom Monitoring Google Google Object Storage and Download Analytics Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page