healthcareai

Aims to make machine learning in healthcare as easy as possible. You can develop customized, reliable, high-performance machine learning models with minimal code. Models are created with automatic preprocessing, hyperparameter tuning, and algorithm selection (between 'xgboost' Chen, T. & Guestrin, C. (2016) , 'ranger' Wright, M. N., & Ziegler, A. (2017) , and 'glm' Friedman J, Hastie T, Tibshirani R. (2010) ) so that they can be easily put into production. Additionally, there are tools to help understand how a model makes its predictions, select prediction threshholds for operational use, and evaluate model performance over time. Code uses 'tidyverse' syntax and most methods have an associated visualization.

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