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Description

End-to-End Automated Machine Learning and Model Evaluation.

Single unified interface for end-to-end modelling of regression, categorical and time-to-event (survival) outcomes. Models created using familiar are self-containing, and their use does not require additional information such as baseline survival, feature clustering, or feature transformation and normalisation parameters. Model performance, calibration, risk group stratification, (permutation) variable importance, individual conditional expectation, partial dependence, and more, are assessed automatically as part of the evaluation process and exported in tabular format and plotted, and may also be computed manually using export and plot functions. Where possible, metrics and values obtained during the evaluation process come with confidence intervals.
Metadata

Version

1.4.8

License

Unknown

Platforms (75)

    Darwin
    FreeBSD
    Genode
    GHCJS
    Linux
    MMIXware
    NetBSD
    none
    OpenBSD
    Redox
    Solaris
    WASI
    Windows
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  • aarch64-darwin
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  • armv5tel-linux
  • armv6l-linux
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  • armv6l-none
  • armv7a-darwin
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  • armv7l-linux
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  • javascript-ghcjs
  • loongarch64-linux
  • m68k-linux
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  • microblaze-linux
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  • rx-none
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