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# StatsLearnModels.jl
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[![Build Status](https://github.com/JuliaML/StatsLearnModels.jl/actions/workflows/CI.yml/badge.svg?branch=main)](https://github.com/JuliaML/StatsLearnModels.jl/actions/workflows/CI.yml?query=branch%3Amain)
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[![Coverage](https://codecov.io/gh/JuliaML/StatsLearnModels.jl/branch/main/graph/badge.svg)](https://codecov.io/gh/JuliaML/StatsLearnModels.jl)
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[![][build-img]][build-url] [![][codecov-img]][codecov-url]
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Statistical learning models for tabular data.
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Statistical learning models for [Tables.jl](https://github.com/JuliaData/Tables.jl) tables.
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## Installation
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Get the latest stable release with Julia's package manager:
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```
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] add StatsLearnModels
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```
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## Usage
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This package provides a `Learn` transform that implements the
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[TableTransforms.jl](https://github.com/JuliaML/TableTransforms.jl)
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interface.
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Given two [Tables.jl](https://github.com/JuliaData/Tables.jl)
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tables with training and test data:
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```julia
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train = (feature1=rand(100), feature2=rand(100), target=rand(1:2, 100))
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test = (feature1=rand(20), feature2=rand(20))
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```
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One can train a learning `model` (e.g. `RandomForestClassifier`) with
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the `train` table:
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```julia
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model = RandomForestClassifier()
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learn = Learn(train, model, ["feature1","feature2"] => "target")
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```
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and apply the trained `model` to the `test` table:
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```julia
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pred = learn(test)
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```
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The package exports native Julia models from various packages
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in the ecosystem. It is also possible to use models from the
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[MLJ.jl](https://github.com/JuliaAI/MLJ.jl) stack.
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The combination of TableTransforms.jl with StatsLearnModels.jl
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can be thought of as a powerful alternative to MLJ.jl.
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[build-img]: https://img.shields.io/github/actions/workflow/status/JuliaML/StatsLearnModels.jl/CI.yml?branch=master&style=flat-square
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[build-url]: https://github.com/JuliaML/StatsLearnModels.jl/actions
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[codecov-img]: https://img.shields.io/codecov/c/github/JuliaML/StatsLearnModels.jl?style=flat-square
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[codecov-url]: https://codecov.io/gh/JuliaML/StatsLearnModels.jl

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