Machine Learning and Tidymodel

Model setting, {Parsnip}

Rpackage Parsnip standardizes model specification. Tidymodel follows the concept of lazy evaluation of the tidyverse. Parsnip sets unified specifications and lately evaluates.

Feature engineering, {Recipes}

Recipes make preprocessing easy with step_() functions. Recipes after specification calculate.

Resampling, {rsample}

To choose a model and hyperparameters, we must validate the different models.

Making hyperparameter set, {dials}

The Rpackage {dials} set hyperparameter similarily with {Parsnip}. {Dials} standadize parameter of each modeling algorithm.

Set modeling process, {Workflowr}

Fit models with hyperparameter, {tunes}

Jun Kang
Clinical Assistant Professor of Hospital Pathology

My research interests include pathology, oncology and statistics.