Metadata-Version: 2.1
Name: smogn
Version: 0.0.1
Summary: A Python implementation of Synthetic Minority Over-Sampling Technique for Regression with Gaussian Noise (SMOGN)
Home-page: https://github.com/nickkunz/smogn
Author: Nick Kunz
Author-email: nick.kunz@columbia.edu
License: GPLv3
Description: # SMOGN: Synthetic Minority Over-Sampling with Gaussian Noise
        
        ## Description
        A Python implementation of Synthetic Minority Over-Sampling Technique for Regression with Gaussian Noise (SMOGN). Conducts the Synthetic Minority Over-Sampling Technique for Regression (SMOTER) with traditional interpolation, as well as with the introduction of Gaussian Noise (SMOTER-GN). Selects between the two over-sampling techniques by the KNN distances underlying a given observation. If the distance is close enough, SMOTER is applied. If too far away, SMOTER-GN is applied. Useful for prediction problems where regression is applicable, but the values in the interest of predicting are rare or uncommon. This can also serve as a useful alternative to log transforming a skewed response variable, especially if generating synthetic data is also of interest.
        <br>
        
        ## Features
        1. The only open-source Python supported version of Synthetic Minority Over-Sampling Technique for Regression
        
        2. Supports Pandas DataFrame inputs containing mixed data types, auto distance metric selection by data type, and optional auto removal of missing values
        
        3. Flexible inputs available to control the areas of interest within a continuous response variable and friendly parameters for over-sampling synthetic data
        
        4. Purely Pythonic, developed for consistency, maintainability, and future improvement, no foreign function calls to C or Fortran, as contained in original R implementation
        <br>
        
        ## Installation
        ```python
        ## install pypi release
        pip install smogn
        
        ## install developer version
        pip install git+https://github.com/nickkunz/smogn.git
        ```
        
        ## Road Map
        1. Distributed computing support
        2. Optimized distance metrics
        3. Explore interpolation methods
        
        ## License
        
        © Nick Kunz, 2019. Licensed under the General Public License v3.0 (GPLv3).
        
        ## Contributions
        
        SMOGN is open for improvements and maintenance. Your help is valued to make the package better for everyone.
        
        ## Reference
        
        Branco, P., Torgo, L., Ribeiro, R. (2017). SMOGN: A Pre-Processing Approach for Imbalanced Regression. Proceedings of Machine Learning Research, 74:36-50. http://proceedings.mlr.press/v74/branco17a/branco17a.pdf.
        
Keywords: over-sampling,synthetic data,machine learning,pre-processing
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Education
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Description-Content-Type: text/markdown
