Metadata-Version: 2.1
Name: bareml
Version: 0.0.2
Summary: A Python module containing various machine learning algorithms.
Home-page: https://github.com/shotahorii/ml-from-scratch
Author: shotahorii
Author-email: sh.sinker@gmail.com
License: MIT
Keywords: machine-learning machine-learning-algorithms machine-learning-from-scratch data-science statistical-models
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Description-Content-Type: text/markdown
Requires-Dist: numpy
Requires-Dist: scipy
Requires-Dist: pandas
Requires-Dist: scikit-learn
Requires-Dist: matplotlib

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**bareml** is a Python module containing various machine learning algorithms implemented from scratch using NumPy.

The implementations are not (and not intended to be) optimised w.r.t. efficiency nor performance. Instead, they are aimed at being as straightforward/transparent as possible. 

## Installation 
```
$ pip install bareml
```

## List of implementations 

### Supervised Learning
- [Bernoulli Naive Bayes](https://github.com/shotahorii/ml-from-scratch/blob/master/bareml/supervised/naive_bayes.py)
- [Decision Trees](https://github.com/shotahorii/ml-from-scratch/blob/master/bareml/supervised/decision_trees.py)
- [Elastic Net](https://github.com/shotahorii/ml-from-scratch/blob/master/bareml/supervised/linear_regression.py)
- [Gaussian Naive Bayes](https://github.com/shotahorii/ml-from-scratch/blob/master/bareml/supervised/naive_bayes.py)
- [Generalised Linear Model](https://github.com/shotahorii/ml-from-scratch/blob/master/bareml/supervised/glm.py)
- [K Nearest Neighbors](https://github.com/shotahorii/ml-from-scratch/blob/master/bareml/supervised/knn.py)
- [Kernel Ridge Regression](https://github.com/shotahorii/ml-from-scratch/blob/master/bareml/supervised/kernel_regression.py)
- [Lasso Regression](https://github.com/shotahorii/ml-from-scratch/blob/master/bareml/supervised/linear_regression.py)
- [Linear Regression](https://github.com/shotahorii/ml-from-scratch/blob/master/bareml/supervised/linear_regression.py)
- [Logistic Regression](https://github.com/shotahorii/ml-from-scratch/blob/master/bareml/supervised/logistic_regression.py)
- [Perceptron](https://github.com/shotahorii/ml-from-scratch/blob/master/bareml/supervised/perceptron.py)
- [Poisson Regression](https://github.com/shotahorii/ml-from-scratch/blob/master/bareml/supervised/glm.py)
- [Ridge Regression](https://github.com/shotahorii/ml-from-scratch/blob/master/bareml/supervised/linear_regression.py)

### Unsupervised Learning
- [KMeans (KMeans++)](https://github.com/shotahorii/ml-from-scratch/blob/master/bareml/unsupervised/kmeans.py)

### Ensemble Learning 
- [AdaBoost](https://github.com/shotahorii/ml-from-scratch/blob/master/bareml/ensemble/adaboost.py)
- [AdaBoost M1](https://github.com/shotahorii/ml-from-scratch/blob/master/bareml/ensemble/adaboost.py)
- [AdaBoost Samme](https://github.com/shotahorii/ml-from-scratch/blob/master/bareml/ensemble/adaboost.py)
- [AdaBoost RT](https://github.com/shotahorii/ml-from-scratch/blob/master/bareml/ensemble/adaboost.py)
- [AdaBoost R2](https://github.com/shotahorii/ml-from-scratch/blob/master/bareml/ensemble/adaboost.py)
- [Bagging](https://github.com/shotahorii/ml-from-scratch/blob/master/bareml/ensemble/baggings.py)
- [Gradient Boosting](https://github.com/shotahorii/ml-from-scratch/blob/master/bareml/ensemble/gradient_boosting.py)
- [Random Forest](https://github.com/shotahorii/ml-from-scratch/blob/master/bareml/ensemble/baggings.py)
- [Stacking](https://github.com/shotahorii/ml-from-scratch/blob/master/bareml/ensemble/stacking.py)
- Voting
- [XGBoost](https://github.com/shotahorii/ml-from-scratch/blob/master/bareml/ensemble/xgboost.py)

