Paper #399
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Paper #399
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bblodfon
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Dec 9, 2024
bblodfon
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Dec 9, 2024
bblodfon
reviewed
Dec 9, 2024
bblodfon
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Dec 9, 2024
bblodfon
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Dec 9, 2024
bblodfon
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Dec 9, 2024
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@sebffischer I finished a first re-writing and text improvement
be-marc
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Dec 16, 2024
paper/paper.md
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| At its core, the `mlr3extralearners` package provides a standardized interface for machine learning and connects many R packages implementing machine learning algorithms into a unified framework. | ||
| The package currently wraps **85 different learning algorithms** from many different R packages, for tasks such as classification, regression, and survival analysis. | ||
| This enables users to seamlessly access and utilize these learners directly within their workflows. | ||
| It also facilitates large-scale empirical benchmark experiments, leveraging the `mlr3` framework's parallelization and optimization capabilities [@benchlargescale]. |
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If we mention large-scale benchmarks in the abstract, we should add a sentence about tuning.
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We mention it in two other spots but I added it also here with a citation, thanks!
be-marc
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Dec 16, 2024
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| The core functionality of `mlr3extralearners` is to integrate new learners into the `mlr3` ecosystem, allowing users to access a wide array of learning algorithms through a unified syntax and standardized interface. | ||
| However, the advantages of `mlr3extralearners` go well beyond simple integration. | ||
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Why don't we add a few codes examples like list_mlr3learners() to show all availble learners? Or show the unified interface with lrn("classif.gbm")?
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We have the link to the website with all learners, this covers the first. The second we could, let's see what Bernd also thinks when he reads it.
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.github/workflows/draft-pdf.ymlfile which renders the paper inpdf(see example github action output), we should remove upon merging