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.
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:
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.
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.