Metadata-Version: 2.1
Name: basicMLpy
Version: 1.0.9
Summary: A collection of simple machine learning algorithms
Home-page: https://github.com/HenrySilvaCS/basicMLpy
Author: Henrique Soares Assumpção e Silva
Author-email: henriquesoares@dcc.ufmg.br
License: UNKNOWN
Platform: UNKNOWN
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.txt
Requires-Dist: numpy (>=1.19)
Requires-Dist: scipy (>=1.5.2)
Requires-Dist: scikit-learn (>=0.23)

# basicMLpy <br />
basicMLpy is a package that implements simple machine learning algorithms. It currently contains eight modules that implement multiple machine learning techniques for supervised learning.<br />
### The basicMLpy.regression module contains the following functionalities:
* Linear Regression 
* Ridge Regression 
### The basicMLpy.classification module contains the following functionalities:
* Multiclass classification through the IRLS(Iteratively Reweighted Least Squares) algorithm
### The basicMLpy.nearest_neighbors module contains the following functionalities:
* An implementation of the K-Nearest Neighbors algorithm, that can fit both classification and regression problems
### The basicMLpy.model_selection module contains the following functionalities:
* A Cross-Validation algorithm for the functions presented by the basicMLpy package
### The basicMLpy.ensemble module contains the following functionalities:
* An implementation of the Random Forests algorithm for regression and classification
* An implementation of the AdaBoost algorithm for classification
* An implementation of the Gradient Boosting algorithm for regression
### The basicMLpy.decomposition module contains the following functionalities:
* An implementation of the SVD decomposition algorithm
* An implementation of the PCA algorithm
### The basicMLpy.loss_functions module contains the following functionalities:
* Multiple functions for error evaluation, e.g. MSE, MAE, exponential loss, etc.
### The basicMLpy.utils module contains the following functionalities:
* Useful functions utilized all throughout the other models.
## Installation <br />
To install basicMLpy run the following command: <br />
`pip install basicMLpy` <br />
## Package's site and documentation <br />
https://henrysilvacs.github.io/basicMLpy/
## Dependencies <br />
basicMLpy requires Python >= 3.8, Numpy >= 1.19, Scipy >= 1.5.2, scikit-learn >= 0.23. <br />
## On Github <br />
https://github.com/HenrySilvaCS/basicMLpy
## On Pypi <br />
https://pypi.org/project/basicMLpy/
## Some thoughts <br />
This is a work in progress project, so more functionalities will be added with time.
## Author <br />
Henrique Soares AssumpÃ§Ã£o e Silva


