Metadata-Version: 2.1
Name: mlhandmade
Version: 0.0.15
Summary: Implemented some ML routines
Author: gittasche
Description-Content-Type: text/markdown
Requires-Dist: scikit-learn (>=1.1.1)
Requires-Dist: numpy (>=1.22.3)
Requires-Dist: scipy (>=1.8.0)
Requires-Dist: matplotlib (>=3.5.1)
Requires-Dist: pandas (>=1.4.2)

# ML-handmade



Implemented some ML routines including other ML stuff such as preprocessing, visualization and model selection.



### References



- Scikit-learn source code: [https://github.com/scikit-learn/scikit-learn](https://github.com/scikit-learn/scikit-learn)

- PRML algorithms by ctgk: [https://github.com/ctgk/PRML](https://github.com/ctgk/PRML)

- Sokolov lectures on ML(RU): [https://github.com/esokolov/ml-course-hse](https://github.com/esokolov/ml-course-hse)

- ML handbook by Yandex SDA(RU): [https://ml-handbook.ru/](https://ml-handbook.ru/)

- MLAlgorithms by rushter: [https://github.com/rushter/MLAlgorithms](https://github.com/rushter/MLAlgorithms)

- mlxtend library by rasbt: [https://github.com/rasbt/mlxtend](https://github.com/rasbt/mlxtend)



### Algorithms implemented



* Linear models with different optmization methods(GD, SGD, Batch-SGD, SAG)

* KNN with three approaches(brute-force, kd-tree, ball-tree)

* Multiclass strategies (One-vs-One, One-vs-Rest)

* Support vector (SVC and $\epsilon$-SVR) with different kernels(Linear, RBF, Polynomial)

* Discriminant analysis(linear & quadratic) implemented using SVD

* Decision tree classifier and regressor

* Random forest classifier and regressor with bootstrap

* AdaBoost classifier and regressor

* Other ML stuff, for instance, k-fold cross validation, quality metrics, plotting, e.t.c



### Installation



It can be installed using `pip`



```bash

pip install mlhandmade

```
