Implemented some ML routines
Project description
ML-handmade
=============
Implemented some ML routines including other ML stuff such as preprocessing, visualization and model selection.
References
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Scikit-learn source code: https://github.com/scikit-learn/scikit-learn
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PRML algorithms by ctgk: https://github.com/ctgk/PRML
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Sokolov lectures on ML(RU): https://github.com/esokolov/ml-course-hse
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ML handbook by Yandex SDA(RU): https://ml-handbook.ru/
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MLAlgorithms by rushter: https://github.com/rushter/MLAlgorithms
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mlxtend library by rasbt: https://github.com/rasbt/mlxtend
Algorithms implemented
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Linear models with different optmization methods(GD, SGD, Batch-SGD, SAG)
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KNN with three approaches(brute-force, kd-tree, ball-tree)
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Multiclass strategies (One-vs-One, One-vs-Rest)
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Support vector (SVC and $\epsilon$-SVR) with different kernels(Linear, RBF, Polynomial)
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Discriminant analysis(linear & quadratic) implemented using SVD
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Decision tree classifier and regressor
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Random forest classifier and regressor with bootstrap
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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
Project details
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