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Project.toml

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name = "MLJLinearModels"
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uuid = "6ee0df7b-362f-4a72-a706-9e79364fb692"
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authors = ["Thibaut Lienart <[email protected]>"]
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version = "0.3.3"
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version = "0.3.4"
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[deps]
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README.md

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| :------------ | :------- | :------------ |
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| [![Build Status](https://travis-ci.org/alan-turing-institute/MLJLinearModels.jl.svg?branch=master)](https://travis-ci.org/alan-turing-institute/MLJLinearModels.jl) | [![codecov.io](http://codecov.io/github/alan-turing-institute/MLJLinearModels.jl/coverage.svg?branch=master)](http://codecov.io/github/alan-turing-institute/MLJLinearModels.jl?branch=master) | TODO |
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This is a convenience package gathering functionalities to solve a number of generalised linear regression/classification problems which, inherently, correspond to an optimisation problem of the form
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This is a package gathering functionalities to solve a number of generalised linear regression/classification problems which, inherently, correspond to an optimisation problem of the form
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```
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L(y, Xθ) + P(θ)
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- focus on performance including in "big data" settings exploiting packages such as [`Optim.jl`](https://github.com/JuliaNLSolvers/Optim.jl), [`IterativeSolvers.jl`](https://github.com/JuliaMath/IterativeSolvers.jl),
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- use a "machine learning" perspective, i.e.: focus essentially on prediction, hyper-parameters should be obtained via a data-driven procedure such as cross-validation.
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All models allow to fit an intercept and allow the penalty to be applied or not on the intercept (not applied by default).
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All models attempt to be efficient in terms of memory allocation to avoid unnecessary copies of the data.
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# NOTES
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This section is only useful if you're interested in implementation details or would like to help extend the library. For usage instruction please head to the docs.
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## Implemented
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docs/build/assets/search.js

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