A collection of simple machine learning algorithms
Project description
basicMLpy
basicMLpy is a package that implements simple machine learning algorithms. It currently contains eight modules that implement multiple machine learning techniques for supervised learning.
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
To install basicMLpy run the following command:
pip install basicMLpy
Package's site and documentation
https://henrysilvacs.github.io/basicMLpy/
Dependencies
basicMLpy requires Python >= 3.8, Numpy >= 1.19, Scipy >= 1.5.2, scikit-learn >= 0.23.
On Github
https://github.com/HenrySilvaCS/basicMLpy
On Pypi
https://pypi.org/project/basicMLpy/
Some thoughts
This is a work in progress project, so more functionalities will be added with time.
Author
Henrique Soares Assumpção e Silva
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