Metadata-Version: 2.1
Name: basicMLpy
Version: 1.0.7
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
Description: # 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
        
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
