Learn

After you know what you need to get started with tidymodels, you can learn more and go further. Find articles here to help you solve specific problems using the tidymodels framework. Articles are organized into these categories:

Perform statistical analysis

Bootstrap resampling and tidy regression models

Apply bootstrap resampling to estimate uncertainty in model parameters.
Hypothesis testing using resampling and tidy data

Perform common hypothesis tests for statistical inference using flexible functions.
Statistical analysis of contingency tables

Use tests of independence and goodness of fit to analyze tables of counts.

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Create robust models

Regression models two ways

Create and train different kinds of regression models with different computational engines.
Classification models using a neural network

Train a classification model and evaluate its performance.
Multivariate analysis using partial least squares

Build and fit a predictive model with more than one outcome.

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Tune, compare, and work with your models

Nested resampling

Estimate the best hyperparameters for a model using nested resampling.
Iterative Bayesian optimization of a classification model

Identify the best hyperparameters for a model using Bayesian optimization of iterative search.

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