parsnip
Get started by learning how to specify and train a model using tidymodels. Read more »
Choose hyperparameters for a model by training on a grid of many possible parameter values. Read more »
Create and train different kinds of regression models with different computational engines. Read more »
Train a classification model and evaluate its performance. Read more »
Create a parsnip model function from an existing model implementation. Read more »
Estimate the best hyperparameters for a model using nested resampling. Read more »
Prepare data for modeling with modular preprocessing steps. Read more »
Measure model performance by generating different versions of the training data through resampling. Read more »
Identify the best hyperparameters for a model using Bayesian optimization of iterative search. Read more »
Improve model performance in imbalanced data sets through undersampling or oversampling. Read more »
Estimate the best values for hyperparameters that cannot be learned directly during model training. Read more »
Develop, from beginning to end, a predictive model using best practices. Read more »
Resources
Explore searchable tables of all tidymodels packages and functions.
Study up on statistics and modeling with our comprehensive books.
Hear the latest about tidymodels packages at the tidyverse blog.