| title |
Developing machine learning models with 'mlapi' |
| author |
Dmitry Selivanov |
| date |
2022-04-24 |
| output |
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| vignette |
%\VignetteIndexEntry{Developing machine learning models with 'mlapi'} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8}
|
Idea of the mlapi package is to provide guideline on how to implement interfaces of the machine learning models in order to have unified consistent flow. API design is mainly borrowed from very successful python scikit-learn package. At the moment scope is limited to the following base classes:
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mlapiEstimation/mlapiEstimationOnline - models which implements supervised learning - regression or classification
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mlapiTransformation/mlapiTransformationOnline - models which learn transformations of the data. For example model can learn TF-IDF on some matrix and apply it to the other holdout matrix
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mlapiDecomposition/mlapiDecompositionOnline - models which decompose input matrix into two matrices (usually low rank). A good example could be matrix factorization where input matrix