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schlosslab
mikropml:User-Friendly R Package for Supervised Machine Learning Pipelines
An interface to build machine learning models for classification and regression problems. 'mikropml' implements the ML pipeline described by Topçuoğlu et al. (2020) <doi:10.1128/mBio.00434-20> with reasonable default options for data preprocessing, hyperparameter tuning, cross-validation, testing, model evaluation, and interpretation steps. See the website <https://www.schlosslab.org/mikropml/> for more information, documentation, and examples.
Maintained by Kelly Sovacool. Last updated 2 years ago.
56 stars 7.83 score 86 scripts
alexzwanenburg
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.
Maintained by Alex Zwanenburg. Last updated 6 months ago.
aiexplainable-aimachine-learningsurvival-analysistabular-data
30 stars 5.03 score 18 scripts