Metadata-Version: 2.1
Name: pyregression_package_GonenRaveh
Version: 1.0.0
Summary: A small package for Mathematical Regression Solver from Multi Features to a single Target feature
Author-email: Gonen Raveh <gonen.raveh@gmail.com>
License: Copyright (c) 2018 The Python Packaging Authority
        
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Project-URL: Homepage, https://github.com/gonenraveh/pyregression
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: scikit-learn
Requires-Dist: matplotlib

# pyregression Package

Logistic Regression (aka logit, MaxEnt) classifier.
In the multiclass case, the training algorithm uses the one-vs-rest (OvR) 
scheme if the ‘multi_class’ option is set to ‘ovr’, and uses the 
cross-entropy loss if the ‘multi_class’ option is set to ‘multinomial’. 
(Currently the ‘multinomial’ option is supported only by the ‘lbfgs’, ‘sag’, ‘saga’ and ‘newton-cg’ solvers.)
This class implements regularized logistic regression using the ‘liblinear’ 
library, ‘newton-cg’, ‘sag’, ‘saga’ and ‘lbfgs’ solvers. Note that regularization 
is applied by default. It can handle both dense and sparse input. Use C-ordered 
arrays or CSR matrices containing 64-bit floats for optimal performance; any other 
input format will be converted (and copied).
The ‘newton-cg’, ‘sag’, and ‘lbfgs’ solvers support only L2 regularization with 
primal formulation, or no regularization. The ‘liblinear’ solver supports both 
L1 and L2 regularization, with a dual formulation only for the L2 penalty. 
The Elastic-Net regularization is only supported by the ‘saga’ solver.
