familiar
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.
- Version1.5.0
- R version≥ 4.0.0
- LicenseEUPL
- Needs compilation?No
- familiar citation info
- Last release09/23/2024
Documentation
- VignetteEvaluation and explanation
- Vignettesource
- VignetteFeature selection methods
- Vignettesource
- VignetteIntroducing familiar
- Vignettesource
- VignetteLearning algorithms and hyperparameter optimisation
- Vignettesource
- VignettePerformance metrics
- Vignettesource
- VignetteUsing familiar prospectively
- Vignettesource
- MaterialNEWS
Team
Alex Zwanenburg
Steffen Löck
Show author detailsRolesAuthorStefan Leger
Show author detailsRolesContributorIram Shahzadi
Show author detailsRolesContributorAsier Rabasco Meneghetti
Show author detailsRolesContributorSebastian Starke
Show author detailsRolesContributorTechnische Universität Dresden
Show author detailsRolesCopyright holderGerman Cancer Research Center
Show author detailsRolesCopyright holder
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Dependencies
- Depends1 package
- Imports5 packages
- Suggests36 packages