Skip to contents

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

Source: R/tidylearn-package.R
tidylearn-package.Rd

Provides a unified tidyverse-compatible interface to R's machine learning ecosystem - from data ingestion to model publishing. The tl_read() family reads data from files ('CSV', 'Excel', 'Parquet', 'JSON'), databases ('SQLite', 'PostgreSQL', 'MySQL', 'BigQuery'), and cloud sources ('S3', 'GitHub', 'Kaggle'). The tl_model() function wraps established implementations from 'glmnet', 'randomForest', 'xgboost', 'e1071', 'rpart', 'gbm', 'nnet', 'cluster', 'dbscan', and others with consistent function signatures and tidy tibble output. Results flow into unified 'ggplot2'-based visualization and optional formatted 'gt' tables via the tl_table() family. 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) doi:10.1023/A:1010933404324 , LASSO regression Tibshirani (1996) doi:10.1111/j.2517-6161.1996.tb02080.x , elastic net Zou and Hastie (2005) doi:10.1111/j.1467-9868.2005.00503.x , support vector machines Cortes and Vapnik (1995) doi:10.1007/BF00994018 , and gradient boosting Friedman (2001) doi:10.1214/aos/1013203451 .

Details

tidylearn wraps established R machine learning packages behind one consistent interface. The main entry points are:

tl_read

Read data from files, databases and cloud sources into a tidy tibble.

tl_model

Fit any supported supervised or unsupervised method.

tl_evaluate

Score a fitted model.

tl_table

Render results as formatted gt tables.

tl_auto_ml

Search across methods automatically.

Every fitted model keeps the underlying package's own object in its $fit slot, so package-specific functionality remains available.

See vignette("getting-started", package = "tidylearn") for a walkthrough.

See also

Useful links:

Author

Maintainer: Cesaire Tobias cesaire@sheetsolved.com