Skip to content

Convex linear models, Linear Regression, Logistic Regression, and Support Vector Machines, to test convexity and performance by using non-linear endogenous features in time series data.

License

Notifications You must be signed in to change notification settings

IFFranciscoME/Convex-Models

Repository files navigation

Convex-Models

Convex linear models, Linear Regression, Logistic Regression, and Support Vector Machines, to test convexity and performance by using non-linear endogenous features in time series data.

Installation

  • Cloning repository

Clone entire github project

git@github.com:IFFranciscoME/Convex-Models.git

(optional) create a virtual environment

virtualenv venv

(optional) activate virtual environment

source ~/venv/bin/activate

and then install dependencies

pip install -r requirements.txt

Example Results

Plot 198
  • The model accuracy with train data was: 82.7 %
  • The model accuracy with test data was: 83.19 %

Author

J.Francisco Munnoz - IFFranciscoME - Is an Associate Professor in the Mathematics and Physics Department, at ITESO University.

License

GNU General Public License v3.0

Permissions of this strong copyleft license are conditioned on making available complete source code of licensed works and modifications, which include larger works using a licensed work, under the same license. Copyright and license notices must be preserved. Contributors provide an express grant of patent rights.

Contact: For more information in reggards of this project, please contact francisco.me@iteso.mx

About

Convex linear models, Linear Regression, Logistic Regression, and Support Vector Machines, to test convexity and performance by using non-linear endogenous features in time series data.

Topics

Resources

License

Stars

Watchers

Forks