tidylearn: A Unified Tidy Interface to R's Machine Learning Ecosystem

Provides a unified tidyverse-compatible interface to R's machine learning packages. Wraps established implementations from 'glmnet', 'randomForest', 'xgboost', 'e1071', 'rpart', 'gbm', 'nnet', 'cluster', 'dbscan', and others - providing consistent function signatures, tidy tibble output, and unified 'ggplot2'-based visualization. The underlying algorithms are unchanged; 'tidylearn' simply makes them easier to use together. Access raw model objects via the $fit slot for package-specific functionality. Methods include random forests Breiman (2001) , LASSO regression Tibshirani (1996) , elastic net Zou and Hastie (2005) , support vector machines Cortes and Vapnik (1995) , and gradient boosting Friedman (2001) .