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Update readme and address #6
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README.md

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@@ -14,72 +14,8 @@ learning framework (such as uploading MLJ runs) see
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[MLJOpenML.jl](https://github.com/JuliaAI/MLJOpenML.jl).
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The code in this repository is based on contributions of Diego Arenas
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**Acknowledgements.** The code in this repository is based on contributions of Diego Arenas
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to [MLJBase.jl](https://github.com/JuliaAI/MLJBase.jl) which do not
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appear in the commit history of this repository.
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Package documentation is [here](https://JuliaAI.github.io/OpenML.jl/dev).
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## Summary of functionality
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- `OpenML.list_tags()`: for listing all dataset tags
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- `OpenML.list_datasets(; tag=nothing, filter=nothing, output_format=...)`: for listing available datasets
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- `OpenML.describe_dataset(id)`: to describe a particular dataset
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- `OpenML.load(id; parser=:arff)`: to download a dataset
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## Installation
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```julia
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using Pkg
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Pkg.add("OpenML")
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```
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If running the demonstration below:
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```julia
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Pkg.add("DataFrames")
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Pkg.add("ScientificTypes")
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```
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## Sample usage
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```julia
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using OpenML # or using MLJ
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using DataFrames
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OpenML.list_tags()
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```
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Listing all datasets with the "OpenML100" tag which also have `n`
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instances and `p` features, where `100 < n < 1000` and `1 < p < 10`:
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```julia
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ds = OpenML.list_datasets(
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tag = "OpenML100",
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filter = "number_instances/100..1000/number_features/1..10",
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output_format = DataFrame)
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```
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Describing and loading one of these datasets:
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```julia
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OpenML.describe_dataset(15)
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table = OpenML.load(15)
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```
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Converting to a data frame:
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```julia
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df = DataFrame(table)
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```
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Inspecting it's schema:
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```julia
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using ScientificTypes
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schema(table)
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```
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Package documentation is [here](https://JuliaAI.github.io/OpenML.jl/stable).

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