mlhandmade 0.1.0

Implemented some ML routines

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

• PRML algorithms by ctgk: https://github.com/ctgk/PRML

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

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

• MLAlgorithms by rushter: https://github.com/rushter/MLAlgorithms

• mlxtend library by rasbt: 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

pip install mlhandmade